WorldWideScience

Sample records for implicit recommendation system

  1. Predictive Blacklisting as an Implicit Recommendation System

    CERN Document Server

    Soldo, Fabio; Markopoulou, Athina

    2009-01-01

    A widely used defense practice against malicious traffic on the Internet is through blacklists: lists of prolific attack sources are compiled and shared. The goal of blacklists is to predict and block future attack sources. Existing blacklisting techniques have focused on the most prolific attack sources and, more recently, on collaborative blacklisting. In this paper, we formulate the problem of forecasting attack sources (also referred to as predictive blacklisting) based on shared attack logs as an implicit recommendation system. We compare the performance of existing approaches against the upper bound for prediction, and we demonstrate that there is much room for improvement. Inspired by the recent Netflix competition, we propose a multi-level prediction model that is adjusted and tuned specifically for the attack forecasting problem. Our model captures and combines various factors, namely: attacker-victim history (using time-series) and attackers and/or victims interactions (using neighborhood models). W...

  2. Social Trust Aware Item Recommendation for Implicit Feedback

    Institute of Scientific and Technical Information of China (English)

    郭磊; 马军; 姜浩然; 陈竹敏; 邢长明

    2015-01-01

    Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Specifically, we first introduce the social trust assumption — a user’s taste is close to the neighbors he/she trusts — into the Bayesian Personalized Ranking model. To explore the impact of users’ multi-faceted trust relations, we further propose a category-sensitive random walk method CRWR to infer the true trust value on each trust link. Finally, we arrive at our trust strength aware item recommendation method SocialBPRCRWR by replacing the raw binary trust matrix with the derived real-valued trust strength. Data analysis and experimental results on two real-world datasets demonstrate the existence of social trust influence and the effectiveness of our social based ranking method SocialBPRCRWR in terms of AUC (area under the receiver operating characteristic curve).

  3. Recommender Systems

    CERN Document Server

    Lü, Linyuan; Yeung, Chi Ho; Zhang, Yi-Cheng; Zhang, Zi-Ke; Zhou, Tao

    2012-01-01

    The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinar...

  4. Recommender systems

    CERN Document Server

    Kembellec, Gérald; Saleh, Imad

    2014-01-01

    Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understan

  5. Archetypal Game Recommender Systems

    DEFF Research Database (Denmark)

    Sifa, Rafet; Bauckhage, C.; Drachen, Anders

    2014-01-01

    Contemporary users (players, consumers) of digital games have thousands of products to choose from, which makes nding games that t their interests challenging. Towards addressing this challenge, in this paper two dierent formulations of Archetypal Analysis for Top-L recommender tasks using implicit...... feedback are presented: factor- and neighborhood-oriented models. These form the rst application of rec- ommender systems to digital games. Both models are tested on a dataset of 500,000 users of the game distribution platform Steam, covering game ownership and playtime data across more than 3000 games...

  6. DEVELOPMENT OF A GENERIC RECOMMENDER SYSTEM

    Directory of Open Access Journals (Sweden)

    Dan Munteanu

    2004-12-01

    Full Text Available his paper presents a recommender system for textual documents taken from web (given as bookmarks. The system uses for classification a combination of content, event and collaborative filters and for recommendation a modified Pearson-r algorithm. It uses implicit and explicit feedback for evaluating documents.

  7. Implicit variational principle for contact Hamiltonian systems

    Science.gov (United States)

    Wang, Kaizhi; Wang, Lin; Yan, Jun

    2017-02-01

    We establish an implicit variational principle for the contact Hamiltonian systems generated by the Hamiltonian H(x, u, p) with respect to the contact 1-form α =\\text{d}u-p\\text{d}x under Tonelli and Lipschitz continuity conditions.

  8. Implicit Gender Stereotypes and Essentialist Beliefs Predict Preservice Teachers' Tracking Recommendations

    Science.gov (United States)

    Nürnberger, Miriam; Nerb, Josef; Schmitz, Florian; Keller, Johannes; Sütterlin, Stefan

    2016-01-01

    This study investigated the extent to which differences in implicit and explicit math--language gender stereotypes, and essentialist beliefs among preservice teachers affect tracking recommendations for math/science versus language-oriented secondary schools. Consistent with expectations, the results suggest that student's gender influences…

  9. Recommender Systems for Learning

    CERN Document Server

    Manouselis, Nikos; Verbert, Katrien; Duval, Erik

    2013-01-01

    Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

  10. Distributed Deliberative Recommender Systems

    Science.gov (United States)

    Recio-García, Juan A.; Díaz-Agudo, Belén; González-Sanz, Sergio; Sanchez, Lara Quijano

    Case-Based Reasoning (CBR) is one of most successful applied AI technologies of recent years. Although many CBR systems reason locally on a previous experience base to solve new problems, in this paper we focus on distributed retrieval processes working on a network of collaborating CBR systems. In such systems, each node in a network of CBR agents collaborates, arguments and counterarguments its local results with other nodes to improve the performance of the system's global response. We describe D2ISCO: a framework to design and implement deliberative and collaborative CBR systems that is integrated as a part of jcolibritwo an established framework in the CBR community. We apply D2ISCO to one particular simplified type of CBR systems: recommender systems. We perform a first case study for a collaborative music recommender system and present the results of an experiment of the accuracy of the system results using a fuzzy version of the argumentation system AMAL and a network topology based on a social network. Besides individual recommendation we also discuss how D2ISCO can be used to improve recommendations to groups and we present a second case of study based on the movie recommendation domain with heterogeneous groups according to the group personality composition and a group topology based on a social network.

  11. Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback

    CERN Document Server

    Hidasi, Balázs

    2012-01-01

    Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few if any implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate diverse context information into the model while maintaining its computational efficiency. In particular, we present two such context-aware implementation variants of iTALS. The first incorporates sea...

  12. Privacy in recommender systems

    NARCIS (Netherlands)

    Jeckmans, Arjan; Beye, Michael; Erkin, Zekeriya; Hartel, Pieter; Lagendijk, Reginald; Tang, Qiang; Ramzan, Naeem; Zwol, van Roelof; Lee, Jong-Seok; Clüver, Kai; Hua, Xian-Sheng

    2013-01-01

    In many online applications, the range of content that is offered to users is so wide that a need for automated recommender systems arises. Such systems can provide a personalized selection of relevant items to users. In practice, this can help people find entertaining movies, boost sales through ta

  13. Implicit quasilinear differential systems: a geometrical approach

    Directory of Open Access Journals (Sweden)

    Miguel C. Munoz-Lecanda

    1999-04-01

    Full Text Available This work is devoted to the study of systems of implicit quasilinear differential equations. In general, no set of initial conditions is admissible for the system. It is shown how to obtain a vector field whose integral curves are the solution of the system, thus reducing the system to one that is ordinary. Using geometrical techniques, we give an algorithmic procedure in order to solve these problems for systems of the form $A(xdot x =alpha (x$ with $A(x$ being a singular matrix. As particular cases, we recover some results of Hamiltonian and Lagrangian Mechanics. In addition, a detailed study of the symmetries of these systems is carried out. This algorithm is applied to several examples arising from technical applications related to control theory.

  14. Discrete-Time Models for Implicit Port-Hamiltonian Systems

    OpenAIRE

    Castaños, Fernando; Michalska, Hannah; Gromov, Dmitry; Hayward, Vincent

    2015-01-01

    Implicit representations of finite-dimensional port-Hamiltonian systems are studied from the perspective of their use in numerical simulation and control design. Implicit representations arise when a system is modeled in Cartesian coordinates and when the system constraints are applied in the form of additional algebraic equations (the system model is in a DAE form). Such representations lend themselves better to sample-data approximations. An implicit representation of a port-Hamiltonian sys...

  15. Towards Geosocial Recommender Systems

    NARCIS (Netherlands)

    Graaff, de Victor; Keulen, van Maurice; By, de Rolf A.

    2012-01-01

    The usage of social networks sites (SNSs), such as Facebook, and geosocial networks (GSNs), such as Foursquare, has increased tremendously over the past years. The willingness of users to share their current locations and experiences facilitate the creation of geographical recommender systems based

  16. Offering A Product Recommendation System in E-commerce

    CERN Document Server

    Dutta, Ruma

    2011-01-01

    This paper proposes a number of explicit and implicit ratings in product recommendation system for Business-to-customer e-commerce purposes. The system recommends the products to a new user. It depends on the purchase pattern of previous users whose purchase pattern is close to that of a user who asks for a recommendation. The system is based on weighted cosine similarity measure to find out the closest user profile among the profiles of all users in database. It also implements Association rule mining rule in recommending the products. Also, this product recommendation system takes into consideration the time of transaction of purchasing the items, thus eliminating sequence recognition problem. Experimental result shows for implicit rating, the proposed method gives acceptable performance in recommending the products. It also shows introduction of association rule improves the performance measure of recommendation system.

  17. Archetypal Game Recommender Systems

    DEFF Research Database (Denmark)

    Sifa, Rafet; Bauckhage, C.; Drachen, Anders

    2014-01-01

    feedback are presented: factor- and neighborhood-oriented models. These form the rst application of rec- ommender systems to digital games. Both models are tested on a dataset of 500,000 users of the game distribution platform Steam, covering game ownership and playtime data across more than 3000 games....... Compared to four other recommender models (nearest neighbor, two popularity mod- els, random baseline), the archetype based models provide the highest recall rates showing that Archetypal Analysis can be successfully applied for Top-L recommendation purposes...

  18. Understanding and using the brief Implicit Association Test: recommended scoring procedures.

    Directory of Open Access Journals (Sweden)

    Brian A Nosek

    Full Text Available A brief version of the Implicit Association Test (BIAT has been introduced. The present research identified analytical best practices for overall psychometric performance of the BIAT. In 7 studies and multiple replications, we investigated analytic practices with several evaluation criteria: sensitivity to detecting known effects and group differences, internal consistency, relations with implicit measures of the same topic, relations with explicit measures of the same topic and other criterion variables, and resistance to an extraneous influence of average response time. The data transformation algorithms D outperformed other approaches. This replicates and extends the strong prior performance of D compared to conventional analytic techniques. We conclude with recommended analytic practices for standard use of the BIAT.

  19. Estimating Probabilities in Recommendation Systems

    CERN Document Server

    Sun, Mingxuan; Kidwell, Paul

    2010-01-01

    Recommendation systems are emerging as an important business application with significant economic impact. Currently popular systems include Amazon's book recommendations, Netflix's movie recommendations, and Pandora's music recommendations. In this paper we address the problem of estimating probabilities associated with recommendation system data using non-parametric kernel smoothing. In our estimation we interpret missing items as randomly censored observations and obtain efficient computation schemes using combinatorial properties of generating functions. We demonstrate our approach with several case studies involving real world movie recommendation data. The results are comparable with state-of-the-art techniques while also providing probabilistic preference estimates outside the scope of traditional recommender systems.

  20. Building a better recommender system in E-commerce

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    This paper presents an architecture of a hybrid recommender system in E-commerce environment. The goal of the system is to make special improvements in giving precisely personalized recommendation through some effective measures.Based on the study on the existing recommendation methods of both the conventional similarity function and the conventional feedback function, several improvement algorithms are developed to enhance the precision of recommendation, which include three improved similarity functions, four improved feedback functions, and adoption of both explicit and implicit preferences in individual user profile. Among them, issues and countermeasures of a new user, prominent preferences and long-term preferences are nicely addressed to gain better recommendation. The user's preferences is so designed to be precisely captured by a user-side agent, and can make self-adjustment with explicit or implicit feedback.

  1. Maximizing profit using recommender systems

    CERN Document Server

    Das, Aparna; Ricketts, Daniel

    2009-01-01

    Traditional recommendation systems make recommendations based solely on the customer's past purchases, product ratings and demographic data without considering the profitability the items being recommended. In this work we study the question of how a vendor can directly incorporate the profitability of items into its recommender so as to maximize its expected profit while still providing accurate recommendations. Our approach uses the output of any traditional recommender system and adjust them according to item profitabilities. Our approach is parameterized so the vendor can control how much the recommendation incorporating profits can deviate from the traditional recommendation. We study our approach under two settings and show that it achieves approximately 22% more profit than traditional recommendations.

  2. Do recommender systems benefit users?

    CERN Document Server

    Yeung, Chi Ho

    2015-01-01

    Recommender systems are present in many web applications to guide our choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products is questionable. Here we introduce a model to examine the benefit of recommender systems for users, and found that recommendations from the system can be equivalent to random draws if one relies too strongly on the system. Nevertheless, with sufficient information about user preferences, recommendations become accurate and an abrupt transition to this accurate regime is observed for some algorithms. On the other hand, we found that a high accuracy evaluated by common accuracy metrics does not necessarily correspond to a high real accuracy nor a benefit for users, which serves as an alarm for operators and researchers of recommender systems. We tested our model with a real dataset and observed similar behaviors. Finally, a recommendation approach with improved accuracy is suggested. These results imply that recommender systems ...

  3. Recommender systems in industrial contexts

    CERN Document Server

    Meyer, Frank

    2012-01-01

    This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: ...

  4. Recommendation systems in software engineering

    CERN Document Server

    Robillard, Martin P; Walker, Robert J; Zimmermann, Thomas

    2014-01-01

    With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data.This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: "Part I - Techniques" introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow.?"Part II - Evaluation" summarizes methods and experimental designs for evaluating recommendations in software engineering.?"Part III - Applications" describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, fo...

  5. Symmetry and reduction in implicit generalized Hamiltonian systems

    NARCIS (Netherlands)

    Blankenstein, G.; Schaft, van der A.J.

    2001-01-01

    In this paper we study the notion of symmetry for implicit generalized Hamiltonian systems, which are Hamiltonian systems with respect to a generalized Dirac structure. We investigate the reduction of these systems admitting a symmetry Lie group with corresponding quantities. Main features in this a

  6. Symmetry and Reduction in Implicit Generalized Hamiltonian Systems

    NARCIS (Netherlands)

    Blankenstein, G.; Schaft, A.J. van der

    2001-01-01

    In this paper we study the notion of symmetry for implicit generalized Hamiltonian systems, which are Hamiltonian systems with respect to a generalized Dirac structure. We investigate the reduction of these systems admitting a symmetry Lie group with corresponding conserved quantities. Main features

  7. IMPLICIT REPRESENTATION FOR THE MODELLING OF HYBRID DYNAMIC SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Hybrid systems can be represented by a discrete event model interacting with a continuous model, and the interface by ideal switching components which modify the topology of a system at the switching time. This paper deals with the modelling of such systems using the bond graph approach. The paper shows the interest of the implicit representation: to derive a unique state equation with jumping parameters, to derive the implicit state equation with index of nilpotency one corresponding to each configuration, to analyze the properties of those models and to compute the discontinuity.

  8. Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction

    OpenAIRE

    Taheri, Seyed Mohammad; Mahyar, Hamidreza; Firouzi, Mohammad; K., Elahe Ghalebi; Grosu, Radu; Movaghar, Ali

    2016-01-01

    Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a fe...

  9. Excursion recommendation system

    OpenAIRE

    Pompe, Grega

    2011-01-01

    Purpose of this thesis was to develop a system, which automatically suggests suitable excursion on base of users location and marks with minimal user load. There are websites with long lists of different excursions, which demand the user to at least briefly know what they want. Application was developed in programing language PHP in conjunction with MySQL database. Ranking system is based on general marks, which are result of positive and negative feedback from users, distance between user an...

  10. User Profiling for Recommendation System

    OpenAIRE

    Kanoje, Sumitkumar; Girase, Sheetal; Mukhopadhyay, Debajyoti

    2015-01-01

    Recommendation system is a type of information filtering systems that recommend various objects from a vast variety and quantity of items which are of the user interest. This results in guiding an individual in personalized way to interesting or useful objects in a large space of possible options. Such systems also help many businesses to achieve more profits to sustain in their filed against their rivals. But looking at the amount of information which a business holds it becomes difficult to...

  11. Context-Aware Recommender Systems

    Science.gov (United States)

    Adomavicius, Gediminas; Tuzhilin, Alexander

    The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms - contextual prefiltering, post-filtering, and modeling - for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

  12. RECOMMENDER SYSTEMS IN SOCIAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Cleomar Valois Batista Jr

    2011-12-01

    Full Text Available The continued and diversified growth of social networks has changed the way in which users interact with them. With these changes, what once was limited to social contact is now used for exchanging ideas and opinions, creating the need for new features. Users have so much information at their fingertips that they are unable to process it by themselves; hence, the need to develop new tools. Recommender systems were developed to address this need and many techniques were used for different approaches to the problem. To make relevant recommendations, these systems use large sets of data, not taking the social network of the user into consideration. Developing a recommender system that takes into account the social network of the user is another way of tackling the problem. The purpose of this project is to use the theory of six degrees of separation (Watts 2003 amongst users of a social network to enhance existing recommender systems.

  13. Tourism recommendation system: empirical investigation

    OpenAIRE

    Petrevska, Biljana; Saso KOCESKI

    2012-01-01

    The paper makes an attempt to justify the necessity of implementing recommendation system which will assist tourists in identification of their ideal holiday. The proposed recommendation system based on collaborative filtering notes positive impulses in the case of Macedonia. A software module is developed being capable to generate a personalized list of favorable and tailor-made items. The research outcomes indicate that the designed national tourism web portal can provide satisfactory perfo...

  14. Introduction on health recommender systems.

    Science.gov (United States)

    Sanchez-Bocanegra, C L; Sanchez-Laguna, F; Sevillano, J L

    2015-01-01

    People are looking for appropriate health information which they are concerned about. The Internet is a great resource of this kind of information, but we have to be careful if we don't want to get harmful info. Health recommender systems are becoming a new wave for apt health information as systems suggest the best data according to the patients' needs.The main goals of health recommender systems are to retrieve trusted health information from the Internet, to analyse which is suitable for the user profile and select the best that can be recommended, to adapt their selection methods according to the knowledge domain and to learn from the best recommendations.A brief definition of recommender systems will be given and an explanation of how are they incorporated in the health sector. A description of the main elementary recommender methods as well as their most important problems will also be made. And, to finish, the state of the art will be described.

  15. Preventing Recommendation Attack in Trust-Based Recommender Systems

    Institute of Scientific and Technical Information of China (English)

    Fu-Guo Zhang

    2011-01-01

    Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation attack.Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations.We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system.To the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender system.We analyze the attack problem,and find that "victim" nodes play a significant role in the attack.Furthermore,we propose a data provenance method to trace malicious users and identify the "victim" nodes as distrust users of recommender system.Feasibility study of the defend method is done with the dataset crawled from Epinions website.

  16. Recommendation Process in SR1 Web Document Recommender System

    OpenAIRE

    Munteanu, Dan

    2008-01-01

    This paper presents a recommender system for web documents (given as bookmarks). The system uses for classification a combination of content, event and collaborative filters and for recommendation a modified Pearson-r algorithm. The algorithm for recommendation is using not only the correlation between users but also the similarity between classes. Some experimental results that support this approach are also presented.

  17. Mining and representing recommendations in actively evolving recommender systems

    DEFF Research Database (Denmark)

    Assent, Ira

    2010-01-01

    ) recommender connections. In this work, we propose mining such active systems to generate easily understandable representations of the recommender network. Users may review these representations to provide active feedback. This approach further enhances the quality of recommendations, especially as topics......Recommender systems provide an automatic means of filtering out interesting items, usually based on past similarity of user ratings. In previous work, we have suggested a model that allows users to actively build a recommender network. Users express trust, obtain transparency, and grow (anonymous...... of interest change over time. Most notably, it extends the amount of control users have over the model that the recommender network builds of their interests....

  18. When are recommender systems useful?

    CERN Document Server

    Blattner, Marcel; Laureti, Paolo

    2007-01-01

    Recommender systems are crucial tools to overcome the information overload brought about by the Internet. Rigorous tests are needed to establish to what extent sophisticated methods can improve the quality of the predictions. Here we analyse a refined correlation-based collaborative filtering algorithm and compare it with a novel spectral method for recommending. We test them on two databases that bear different statistical properties (MovieLens and Jester) without filtering out the less active users and ordering the opinions in time, whenever possible. We find that, when the distribution of user-user correlations is narrow, simple averages work nearly as well as advanced methods. Recommender systems can, on the other hand, exploit a great deal of additional information in systems where external influence is negligible and peoples' tastes emerge entirely. These findings are validated by simulations with artificially generated data.

  19. TOURISM RECOMMENDATION SYSTEM: EMPIRICAL INVESTIGATION

    Directory of Open Access Journals (Sweden)

    Biljana PETREVSKA

    2012-12-01

    Full Text Available The paper makes an attempt to justify the necessity of implementing recommendation system which will assist tourists in identification of their ideal holiday. The proposed recommendation system based on collaborative filtering notes positive impulses in the case of Macedonia. A software module is developed being capable to generate a personalized list of favorable and tailor-made items. The research outcomes indicate that the designed national tourism web portal can provide satisfactory performance and may be of high importance to all key-tourism actors in the process of identifying measures necessary for creating competitive tourism product.

  20. Ubiquitous Multicriteria Clinic Recommendation System.

    Science.gov (United States)

    Chen, Toly

    2016-05-01

    Advancements in information, communication, and sensor technologies have led to new opportunities in medical care and education. Patients in general prefer visiting the nearest clinic, attempt to avoid waiting for treatment, and have unequal preferences for different clinics and doctors. Therefore, to enable patients to compare multiple clinics, this study proposes a ubiquitous multicriteria clinic recommendation system. In this system, patients can send requests through their cell phones to the system server to obtain a clinic recommendation. Once the patient sends this information to the system, the system server first estimates the patient's speed according to the detection results of a global positioning system. It then applies a fuzzy integer nonlinear programming-ordered weighted average approach to assess four criteria and finally recommends a clinic with maximal utility to the patient. The proposed methodology was tested in a field experiment, and the experimental results showed that it is advantageous over two existing methods in elevating the utilities of recommendations. In addition, such an advantage was shown to be statistically significant.

  1. Recommender Systems using Graph Theory

    Directory of Open Access Journals (Sweden)

    Vishal Venkatraman

    2013-08-01

    Full Text Available Recommender systems have become one of the important tools in E-Commerce. They combine the ratings of services or products by one user with the ratings from other users to answer similar interest queries with predictions and suggestions. The users thus receive anonymous recommendations from people similar interests. Even though this process seems unobjectionable, it aggregates user preferences, which can be tapped to recognise information about a particular user. Users who rate products or services across different types or domains in the systems are the major victims for this exploitation. We could determine the advantages and risks by performing a detailed analysis with a particular recommendation algorithm, but it would be difficult to draw general conclusions from this approach. In this paper, we aim for an algorithm independent analysis by applying a graph-theoretic model. By employing this model, we show that a user benefits most from recommendations based on similarity between the various products rated by the users. This paper tries to draw a graph through the various items rated by the users and finds the items that are most common among the user and his friends which is then recommended to him.

  2. Scientific and educational recommender systems

    Science.gov (United States)

    Guseva, A. I.; Kireev, V. S.; Bochkarev, P. V.; Kuznetsov, I. A.; Philippov, S. A.

    2017-01-01

    This article discusses the questions associated with the use of reference systems in the preparation of graduates in physical function. The objective of this research is creation of model of recommender system user from the sphere of science and education. The detailed review of current scientific and social network for scientists and the problem of constructing recommender systems in this area. The result of this study is to research user information model systems. The model is presented in two versions: the full one - in the form of a semantic network, and short - in a relational form. The relational model is the projection in the form of semantic network, taking into account the restrictions on the amount of bonds that characterize the number of information items (research results), which interact with the system user.

  3. The SAPO Campus Recommender System: A Study about Students' and Teachers' Opinions

    Science.gov (United States)

    Pedro, Luís; Santos, Carlos; Almeida, Sara Filipa; Ramos, Fernando; Moreira, António; Almeida, Margarida; Antunes, Maria João

    2014-01-01

    This paper aims to assess the relevance and usefulness of the SAPO Campus recommender system, through the analysis of students' and teachers' opinions. Recommender systems, assuming a "technology-driven" approach, have been designed with the primary goal of predicting user interests based on the implicit analysis of their actions and…

  4. Customization Using Fuzzy Recommender Systems

    Institute of Scientific and Technical Information of China (English)

    Ronald R. Yager

    2004-01-01

    We discuss some methods for constructing recommender systems. An important feature of the methods studied here is that we assume the availability of a description, representation, of the objects being considered for recommendation. The approaches studied here differ from collaborative filtering in that we only use pReferences information from the individual for whom we are providing the recommendation and make no use the preferences of other collaborators. We provide a detailed discussion of the construction of the representation schema used. We consider two sources of information about the users preferences. The first are direct statements about the type of objects the user likes. The second source of information comes from ratings of objects which the user has experienced.

  5. Recommendation Process in SR1 Web Document Recommender System

    Directory of Open Access Journals (Sweden)

    Dan MUNTEANU

    2008-12-01

    Full Text Available This paper presents a recommender system for web documents (given as bookmarks. The system uses for classification a combination of content, event and collaborative filters and for recommendation a modified Pearson-r algorithm. The algorithm for recommendation is using not only the correlation between users but also the similarity between classes. Some experimental results that support this approach are also presented.

  6. Classification of Recommender Expertise in the Wikipedia Recommender System

    DEFF Research Database (Denmark)

    Jensen, Christian D.; Pilkauskas, Povilas; Lefevre, Thomas

    2011-01-01

    to the quality of articles. The Wikipedia Recommender System (WRS) was developed to help users determine the credibility of articles based on feedback from other Wikipedia users. The WRS implements a collaborative filtering system with trust metrics, i.e., it provides a rating of articles "which emphasizes...... feedback from recommenders that the user has agreed with in the past. This exposes the problem that most recommenders are not equally competent in all subject areas. The first WRS prototype did not include an evaluation of the areas of expertise of recommenders, so the trust metric used in the article...... ratings reflected the average competence of recommenders across all subject areas. We have now developed a new version of the WRS, which evaluates the expertise of recommenders within different subject areas. In order to do this, we need to identify a way to classify the subject area of all the articles...

  7. A Flexible Electronic Commerce Recommendation System

    Science.gov (United States)

    Gong, Songjie

    Recommendation systems have become very popular in E-commerce websites. Many of the largest commerce websites are already using recommender technologies to help their customers find products to purchase. An electronic commerce recommendation system learns from a customer and recommends products that the customer will find most valuable from among the available products. But most recommendation methods are hard-wired into the system and they support only fixed recommendations. This paper presented a framework of flexible electronic commerce recommendation system. The framework is composed by user model interface, recommendation engine, recommendation strategy model, recommendation technology group, user interest model and database interface. In the recommender strategy model, the method can be collaborative filtering, content-based filtering, mining associate rules method, knowledge-based filtering method or the mixed method. The system mapped the implementation and demand through strategy model, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.

  8. WRS: The Wikipedia Recommender System

    Science.gov (United States)

    Lefévre, Thomas; Jensen, Christian Damsgaard; Korsgaard, Thomas Rune

    In 2005, the Wikipedia became the most popular reference website on the Internet and it has continued to grow in size and popularity ever since. With the increasing reliance on the Wikipedia comes issues of the credibility and provenance of content. In order to address these issues, we have developed a Recommender System for the Wikipedia, which allows the users of the Wikipedia to rate articles in order to guide other users about the quality of articles. This rating system provides both an incentive for authors to improve articles and a quantifiable measure of the perceived quality of articles.

  9. Recommender systems in knowledge-mining

    Science.gov (United States)

    Volna, Eva

    2017-07-01

    The subject of the paper is to analyse the possibilities of application of recommender systems in the field of data mining. The work focuses on three basic types of recommender systems (collaborative, content-based and hybrid). The goal of the article is to evaluate which of these three concepts of recommender systems provides forecast with the lowest error rate in the domain of recommending movies. This target is fulfilled by the practical part of the work - at first, the own recommender system was designed and created, capable of obtaining movies recommendation from the database based on the user's preferences. Next, we verified experimentally which recommender system produces more accurate results.

  10. Ontological Matchmaking in Recommender Systems

    CERN Document Server

    Bonifati, Angela; Sileo, Domenica; Summa, Gianvito

    2010-01-01

    The electronic marketplace offers great potential for the recommendation of supplies. In the so called recommender systems, it is crucial to apply matchmaking strategies that faithfully satisfy the predicates specified in the demand, and take into account as much as possible the user preferences. We focus on real-life ontology-driven matchmaking scenarios and identify a number of challenges, being inspired by such scenarios. A key challenge is that of presenting the results to the users in an understandable and clear-cut fashion in order to facilitate the analysis of the results. Indeed, such scenarios evoke the opportunity to rank and group the results according to specific criteria. A further challenge consists of presenting the results to the user in an asynchronous fashion, i.e. the 'push' mode, along with the 'pull' mode, in which the user explicitly issues a query, and displays the results. Moreover, an important issue to consider in real-life cases is the possibility of submitting a query to multiple p...

  11. Cryptographically-enhanced privacy for recommender systems

    NARCIS (Netherlands)

    Jeckmans, Adrianus Johannus Paulus

    2014-01-01

    Automated recommender systems are used to help people find interesting content or persons in the vast amount of information available via the internet. There are different types of recommender systems, for example collaborative filtering systems and content-based recommender systems. However, all re

  12. Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System

    Directory of Open Access Journals (Sweden)

    Mahdi Nasiri

    2016-06-01

    Full Text Available Background: In today’s world, chronic diseases are predominant health problems and cause heavy burden on society; therefore early diagnosis and even prediction of the disease is a way to reduce this burden. In this project, we tried to use recommender system to predict which other diseases a chronic patient is susceptible for. Methods: In this study, through a dynamic recommender system, we evaluated patients’ treatment destiny during the time. Results: It was shown that our method increased accuracy and reduced error compared with other recommendation methods in disease prediction. Conclusion: Compared to current usual methods, in our method we used previous patients’ characteristics as one of the factorization variables to predict destiny of future patients. Furthermore, using this method, we can predict which complication or disease the patient would suffer from first in future. Therefore, we can manage policies toward disease burden reduction by implementing prevention programs.

  13. Classification of Recommender Expertise in the Wikipedia Recommender System

    DEFF Research Database (Denmark)

    Jensen, Christian D.; Pilkauskas, Povilas; Lefévre, Thomas

    2011-01-01

    to the quality of articles. The Wikipedia Recommender System (WRS) was developed to help users determine the credibility of articles based on feedback from other Wikipedia users. The WRS implements a collaborative filtering system with trust metrics, i.e., it provides a rating of articles which emphasizes...... ratings reflected the average competence of recommenders across all subject areas. We have now developed a new version of the WRS, which evaluates the expertise of recommenders within different subject areas. In order to do this, we need to identify a way to classify the subject area of all the articles......The Wikipedia is a web-based encyclopedia, written and edited collaboratively by Internet users. The Wikipedia has an extremely open editorial policy that allows anybody, to create or modify articles. This has promoted a broad and detailed coverage of subjects, but also introduced problems relating...

  14. On Differentially Private Online Collaborative Recommendation Systems

    OpenAIRE

    Gilbert, Seth; Liu, Xiao; Yu, Haifeng

    2015-01-01

    In collaborative recommendation systems, privacy may be compromised, as users' opinions are used to generate recommendations for others. In this paper, we consider an online collaborative recommendation system, and we measure users' privacy in terms of the standard differential privacy. We give the first quantitative analysis of the trade-offs between recommendation quality and users' privacy in such a system by showing a lower bound on the best achievable privacy for any non-trivial algorith...

  15. An Automated Recommender System for Course Selection

    Directory of Open Access Journals (Sweden)

    Amer Al-Badarenah

    2016-03-01

    Full Text Available Most of electronic commerce and knowledge management` systems use recommender systems as the underling tools for identifying a set of items that will be of interest to a certain user. Collaborative recommender systems recommend items based on similarities and dissimilarities among users’ preferences. This paper presents a collaborative recommender system that recommends university elective courses to students by exploiting courses that other similar students had taken. The proposed system employs an association rules mining algorithm as an underlying technique to discover patterns between courses. Experiments were conducted with real datasets to assess the overall performance of the proposed approach.

  16. Recommender Systems for Social Tagging Systems

    CERN Document Server

    Balby Marinho, Leandro

    2012-01-01

    Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known appl

  17. Persuasive Recommender Systems Conceptual Background and Implications

    CERN Document Server

    Yoo, Kyung-Hyan; Zanker, Markus

    2013-01-01

    Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding influential factors in advice seeking relationships, which is abundant in the context of human-human relationships, can provide an important framework for identifying potential influence factors in recommender system context. This book reviews the existing literature on the factors in advice seeking relationships in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are dis...

  18. Panorama of Recommender Systems to Support Learning

    NARCIS (Netherlands)

    Drachsler, Hendrik; Verbert, Katrien; Santos, Olga C.; Manouselis, Nikos

    2015-01-01

    This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender syst

  19. A Survey Paper on Recommender Systems

    CERN Document Server

    Almazro, Dhoha; Albdulkarim, Lamia; Kherees, Mona; Martinez, Romy; Nzoukou, William

    2010-01-01

    Recommender systems apply data mining techniques and prediction algorithms to predict users' interest on information, products and services among the tremendous amount of available items. The vast growth of information on the Internet as well as number of visitors to websites add some key challenges to recommender systems. These are: producing accurate recommendation, handling many recommendations efficiently and coping with the vast growth of number of participants in the system. Therefore, new recommender system technologies are needed that can quickly produce high quality recommendations even for huge data sets. To address these issues we have explored several collaborative filtering techniques such as the item based approach, which identify relationship between items and indirectly compute recommendations for users based on these relationships. The user based approach was also studied, it identifies relationships between users of similar tastes and computes recommendations based on these relationships. In...

  20. Stability of Semi-Implicit and Iterative Centred-Implicit Time Discretizations for Various Equation Systems Used in NWP

    CERN Document Server

    Benard, P

    2003-01-01

    The stability of classical semi-implicit scheme, and some more advanced iterative schemes recently proposed for Numerical Weather Prediction (NWP) purpose is examined. In all these schemes, the solution of the centred-implicit non-linear equation is approached by an iterative fixed-point algorithm, preconditioned by a simple, constant in time, linear operator. A general methodology for assessing analytically the stability of these schemes on canonical problems for a vertically unbounded atmosphere is presented. The proposed method is valid for all the equation systems usually employed in NWP. However, as in earlier studies, the method can be applied only in simplified meteorological contexts, thus overestimating the actual stability that would occur in more realistic meteorological contexts. The analysis is performed in the spatially-continuous framework, hence allowing to eliminate the spatial-discretisation or the boundary conditions as possible causes of the fundamental instabilities linked to the time-sch...

  1. The Definition of Novelty in Recommendation System

    Directory of Open Access Journals (Sweden)

    Liang Zhang

    2013-01-01

    Full Text Available With the development of information technology and application of the Internet, People gradually entered the time of information overload from information scarcity. User satisfaction with recommender systems is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. Novelty is one of the important metrics of customer satisfaction. There is an increasing realization in the Recommender Systems (RS field that novelty is fundamental qualities of recommendation effectiveness and added-value. This paper combed research results about definition and algorithm of novel recommendation, and starting from the meaning of "novel", defined novelty of item in recommendation system. Experiment proved using the definition of novelty to recommend can effectively recognize the item that the user is familiar with and ensure certain accuracy.

  2. An Agent Framework of Tourism Recommender System

    Directory of Open Access Journals (Sweden)

    Jia Zhi Yang

    2016-01-01

    Full Text Available This paper proposes the development of an Agent framework for tourism recommender system. The recommender system can be featured as an online web application which is capable of generating a personalized list of preference attractions for tourists. Traditional technologies of classical recommender system application domains, such as collaborative filtering, content-based filtering and content-based filtering are effectively adopted in the framework. In the framework they are constructed as Agents that can generate recommendations respectively. Recommender Agent can generate recommender information by integrating the recommendations of Content-based Agent, collaborative filtering-based Agent and constraint-based Agent. In order to make the performance more effective, linear combination method of data fusion is applied. User interface is provided by the tourist Agent in form of webpages and mobile app.

  3. Recommendation system for trip planning

    OpenAIRE

    Hlupič, Marko

    2011-01-01

    The thesis describes a web application used for recommending tourist destinations and trip planning. Given that one location offers several tourist attractions and activities, the destination is divided into a location, containing only the name and the corresponding region, and the possible activity that could take place in that location. The users choose some activities and later on decide whether these locations met their expectations or not. The application is divided into two parts: the f...

  4. RECOMMENDER SYSTEMS IN E-COMMERCE APPLICATIONS

    Directory of Open Access Journals (Sweden)

    Gyöngyvér KOVÁCS

    2016-12-01

    Full Text Available One of the major problem with online shopping is finingd the right product, because finding the right product presumes that we know its name, but in most cases it is not so. For this reason the users need help in the process of online searching/shopping. Recommender systems have became a popular technique and strategy for helping users to select desirable products or services. In the past few years the recommender systems have changed from novelties used by a few big e-commerce sites, to serious business tools that are re-shaping the world of e-commerce. In this paper, we provide a brief overview of the classification of recommendation systems based on technology used to create recommendations, and inputs they need from the customers. Furthermore we analyze a few algorithms used by recommender systems and we will also present some marketing recommender systems and their comparative analysis.

  5. Kernel based collaborative recommender system for -purchasing

    Indian Academy of Sciences (India)

    M K Kavitha Devi; P Venkatesh

    2010-10-01

    Recommender system a new marketing strategy plays an important role particularly in an electronic commerce environment. Among the various recommender systems, collaborative recommender system (CRS) is widely used in a number of different applications such as recommending web pages, movies, tapes and items. CRS suffers from scalability, sparsity, and cold start problems. An intelligent integrated recommendation approach using radial basis function network (RBFN) and collaborative filtering (CF), based on Cover’s theorem, is proposed in order to overcome the traditional problems of CRS. The proposed system predicts the trend by considering both likes and dislikes of the active user. The empirical evaluation results reveal that the proposed approach is more effective than other existing approaches in terms of accuracy and relevance measure of recommendations.

  6. Privacy Preserving Recommendation System Based on Groups

    OpenAIRE

    Shang, Shang; Hui, Yuk; Hui, Pan; Cuff, Paul; Kulkarni, Sanjeev

    2013-01-01

    Recommendation systems have received considerable attention in the recent decades. Yet with the development of information technology and social media, the risk in revealing private data to service providers has been a growing concern to more and more users. Trade-offs between quality and privacy in recommendation systems naturally arise. In this paper, we present a privacy preserving recommendation framework based on groups. The main idea is to use groups as a natural middleware to preserve ...

  7. Uncovering the information core in recommender systems

    Science.gov (United States)

    Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhou, Tao

    2014-08-01

    With the rapid growth of the Internet and overwhelming amount of information that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in online systems. So far, much attention has been paid to designing new recommendation algorithms and improving existent ones. However, few works considered the different contributions from different users to the performance of a recommender system. Such studies can help us improve the recommendation efficiency by excluding irrelevant users. In this paper, we argue that in each online system there exists a group of core users who carry most of the information for recommendation. With them, the recommender systems can already generate satisfactory recommendation. Our core user extraction method enables the recommender systems to achieve 90% of the accuracy of the top-L recommendation by taking only 20% of the users into account. A detailed investigation reveals that these core users are not necessarily the large-degree users. Moreover, they tend to select high quality objects and their selections are well diversified.

  8. Recommendation System Based on Fuzzy Cognitive Map

    Directory of Open Access Journals (Sweden)

    Wei Liu

    2014-07-01

    Full Text Available With the increase of data volume and visitor volume, the website faces great challenge in the environment of network. How to know the users’ requirements rapidly and effectively and recommend the required information to the user becomes the research direction of all websites. The researchers of recommendation system propose a series of recommendation system models and algorithms for the user. The common challenge faced by these algorithms is how to judge the user intention and recommend the relevant content by little user action. The paper proposes the user situation awareness and information recommendation system based on fuzzy clustering analysis and fuzzy cognitive maps, and verifies the validity of the algorithm by the application to recommendation site of academic thesis.

  9. A potential implicit particle method for high-dimensional systems

    Science.gov (United States)

    Weir, B.; Miller, R. N.; Spitz, Y. H.

    2013-11-01

    This paper presents a particle method designed for high-dimensional state estimation. Instead of weighing random forecasts by their distance to given observations, the method samples an ensemble of particles around an optimal solution based on the observations (i.e., it is implicit). It differs from other implicit methods because it includes the state at the previous assimilation time as part of the optimal solution (i.e., it is a lag-1 smoother). This is accomplished through the use of a mixture model for the background distribution of the previous state. In a high-dimensional, linear, Gaussian example, the mixture-based implicit particle smoother does not collapse. Furthermore, using only a small number of particles, the implicit approach is able to detect transitions in two nonlinear, multi-dimensional generalizations of a double-well. Adding a step that trains the sampled distribution to the target distribution prevents collapse during the transitions, which are strongly nonlinear events. To produce similar estimates, other approaches require many more particles.

  10. Recommendation-Aware Smartphone Sensing System

    Directory of Open Access Journals (Sweden)

    Mu-Yen Chen

    2014-12-01

    Full Text Available The context-aware concept is to reduce the gap between users and information systems so that the information systems actively get to understand users’ context and demand and in return provide users with better experience. This study integrates the concept of context-aware with association algorithms to establish the context-aware recommendation systems (CARS. The CARS contains three modules and provides the product recommendations for users with their smartphone. First, the simple RSSI Indoor localization module (SRILM locates the user position and detects the context information surrounding around users. Second, the Apriori recommendation module (ARM provides effective recommended product information for users through association rules mining. The appropriate product information can be received effectiveness and greatly enhanced the recommendation service.

  11. Recommender Systems for the Social Web

    CERN Document Server

    Pazos Arias, José J; Díaz Redondo, Rebeca P

    2012-01-01

    The recommendation of products, content and services cannot be considered newly born, although its widespread application is still in full swing. While its growing success in numerous sectors, the progress of the  Social Web has revolutionized the architecture of participation and relationship in the Web, making it necessary to restate recommendation and reconciling it with Collaborative Tagging, as the popularization of authoring in the Web, and  Social Networking, as the translation of personal relationships to the Web. Precisely, the convergence of recommendation with the above Social Web pillars is what motivates this book, which has collected contributions from well-known experts in the academy and the industry to provide a broader view of the problems that Social Recommenders might face with.  If recommender systems have proven their key role in facilitating the user access to resources on the Web, when sharing resources has become social, it is natural for recommendation strategies in the Social Web...

  12. Classification of Recommender Expertise in the Wikipedia Recommender System

    DEFF Research Database (Denmark)

    Jensen, Christian D.; Pilkauskas, Povilas; Lefévre, Thomas

    2011-01-01

    feedback from recommenders that the user has agreed with in the past. This exposes the problem that most recommenders are not equally competent in all subject areas. The first WRS prototype did not include an evaluation of the areas of expertise of recommenders, so the trust metric used in the article...... ratings reflected the average competence of recommenders across all subject areas. We have now developed a new version of the WRS, which evaluates the expertise of recommenders within different subject areas. In order to do this, we need to identify a way to classify the subject area of all the articles...

  13. Semantic Grounding Strategies for Tagbased Recommender Systems

    CERN Document Server

    Durao, Frederico

    2011-01-01

    Recommender systems usually operate on similarities between recommended items or users. Tag based recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases. Therefore, similarities computed without their semantic groundings might lead to less relevant recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study besides other things reveals that currently available OWL ontologies are very narrow and the percentage of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as it does not support several semantic relationships. Furthermore, the study reveals that even with such number of expansions, the recommendations change considerably.

  14. Effective Personalized Recommendation in Collaborative Tagging Systems

    CERN Document Server

    Zhang, Zi-Ke

    2009-01-01

    Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and are therefore potential to help in improving better personalized recommendations. In this paper, we propose a tag-based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset: Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.

  15. A Flexible Recommendation System for Cable TV

    OpenAIRE

    Goncalves, Diogo; Costa, Miguel; Couto, Francisco M.

    2016-01-01

    Recommendation systems are being explored by Cable TV operators to improve user satisfaction with services, such as Live TV and Video on Demand (VOD) services. More recently, Catch-up TV has been introduced, allowing users to watch recent broadcast content whenever they want to. These services give users a large set of options from which they can choose from, creating an information overflow problem. Thus, recommendation systems arise as essential tools to solve this problem by helping users ...

  16. Recent developments in affective recommender systems

    Science.gov (United States)

    Katarya, Rahul; Verma, Om Prakash

    2016-11-01

    Recommender systems (RSs) are playing a significant role since 1990s as they provide relevant, personalized information to the users over the internet. Lots of work have been done in information filtering, utilization, and application related to RS. However, an important area recently draws our attention which is affective recommender system. Affective recommender system (ARS) is latest trending area of research, as publication in this domain are few and recently published. ARS is associated with human behaviour, human factors, mood, senses, emotions, facial expressions, body gesture and physiological with human-computer interaction (HCI). Due to this assortment and various interests, more explanation is required, as it is in premature phase and growing as compared to other fields. So we have done literature review (LR) in the affective recommender systems by doing classification, incorporate reputed articles published from the year 2003 to February 2016. We include articles which highlight, analyse, and perform a study on affective recommender systems. This article categorizes, synthesizes, and discusses the research and development in ARS. We have classified and managed ARS papers according to different perspectives: research gaps, nature, algorithm or method adopted, datasets, the platform on executed, types of information and evaluation techniques applied. The researchers and professionals will positively support this survey article for understanding the current position, research in affective recommender systems and will guide future trends, opportunity and research focus in ARS.

  17. Machine learning paradigms applications in recommender systems

    CERN Document Server

    Lampropoulos, Aristomenis S

    2015-01-01

    This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and ...

  18. RECOMMENDATION SYSTEM USING BLOOM FILTER IN MAPREDUCE

    Directory of Open Access Journals (Sweden)

    Reena Pagare

    2013-11-01

    Full Text Available Many clients like to use the Web to discover product details in the form of online reviews. The reviews are provided by other clients and specialists. Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information facilities. Collaborative filtering methods are vital component in recommender systems as they generate high-quality recommendations by influencing the likings of society of similar users. The collaborative filtering method has assumption that people having same tastes choose the same items. The conventional collaborative filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is required to deal with the sparse data problem & produce high quality recommendations in large scale mobile environment. MapReduce is a programming model which is widely used for large-scale data analysis. The described algorithm of recommendation mechanism for mobile commerce is user based collaborative filtering using MapReduce which reduces scalability problem in conventional CF system. One of the essential operations for the data analysis is join operation. But MapReduce is not very competent to execute the join operation as it always uses all records in the datasets where only small fraction of datasets are applicable for the join operation. This problem can be reduced by applying bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will improve the join performance.

  19. All-stages-implicit and strong-stability-preserving implicit-explicit Runge-Kutta time discretization schemes for hyperbolic systems with stiff relaxation terms

    CERN Document Server

    Duan, Shu-Chao

    2016-01-01

    We construct eight implicit-explicit (IMEX) Runge-Kutta (RK) schemes up to third order of the type in which all stages are implicit so that they can be used in the zero relaxation limit in a unified and convenient manner. These all-stages-implicit (ASI) schemes attain the strong-stability-preserving (SSP) property in the limiting case, and two are SSP for not only the explicit part but also the implicit part and the entire IMEX scheme. Three schemes can completely recover to the designed accuracy order in two sides of the relaxation parameter for both equilibrium and non-equilibrium initial conditions. Two schemes converge nearly uniformly for equilibrium cases. These ASI schemes can be used for hyperbolic systems with stiff relaxation terms or differential equations with some type constraints.

  20. A Personalized System for Conversational Recommendations

    CERN Document Server

    Goker, M H; Thompson, C A; 10.1613/jair.1318

    2011-01-01

    Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and nu...

  1. Personalized Academic Research Paper Recommendation System

    OpenAIRE

    Lee, Joonseok; Lee, Kisung; Kim, Jennifer G.

    2013-01-01

    A huge number of academic papers are coming out from a lot of conferences and journals these days. In these circumstances, most researchers rely on key-based search or browsing through proceedings of top conferences and journals to find their related work. To ease this difficulty, we propose a Personalized Academic Research Paper Recommendation System, which recommends related articles, for each researcher, that may be interesting to her/him. In this paper, we first introduce our web crawler ...

  2. A Fast Implicit Integration Scheme to Solve Highly Nonlinear System

    Science.gov (United States)

    Siddiquee, Saiful

    Now-a-days researchers are formulating new generation of soil-models based on combined theory. That means researchers are trying to put forward a unified material model, which would predict at least the behaviour of all types of soils under all types of stress and time paths. So the solution techniques so far being used by the nonlinear Finite Element packages no longer can meet the huge demand of computational speed created by those models. It was necessary to develop a new type of solution scheme for the sophisticated models. Usually material nonlinearity makes it difficult to create a robust solution technique. So it is important to develop a solution scheme which will be very robust at the same time. That means the solution scheme should not break-down even for a notoriously complicated unified model. In this paper, we have developed an implicit solution scheme, which solves the resulting nonlinear equations of motion by implicit dynamic relaxation. There are a myriad number of implicit schemes for the use. Here a relatively less used method—called "Houbolt's integration scheme" has been used. It is very similar to the central difference scheme only difference is the use of the higher-order terms in the definition of velocity and acceleration. In order to make it faster, sparse-matrix solution scheme is used with partial pivoting and reordering of matrix elements to minimize the fill-ins. The combined effect is quite dramatic. It provides the main two traits of a good nonlinear solution technique—i.e., speed and robustness of solution. The solution scheme is applied to trace the full loading path of an elasto-visco-plastically defined material behaviour of a Plane Strain Compression (PSC) test sample. There is a huge gain in speed and robustness compared to the other techniques of solution.

  3. Collaborative User Network Embedding for Social Recommender Systems

    KAUST Repository

    Zhang, Chuxu

    2017-06-09

    To address the issue of data sparsity and cold-start in recommender system, social information (e.g., user-user trust links) has been introduced to complement rating data for improving the performances of traditional model-based recommendation techniques such as matrix factorization (MF) and Bayesian personalized ranking (BPR). Although effective, the utilization of the explicit user-user relationships extracted directly from such social information has three main limitations. First, it is difficult to obtain explicit and reliable social links. Only a small portion of users indicate explicitly their trusted friends in recommender systems. Second, the “cold-start” users are “cold” not only on rating but also on socializing. There is no significant amount of explicit social information that can be useful for “cold-start” users. Third, an active user can be socially connected with others who have different taste/preference. Direct usage of explicit social links may mislead recommendation. To address these issues, we propose to extract implicit and reliable social information from user feedbacks and identify top-k semantic friends for each user. We incorporate the top-k semantic friends information into MF and BPR frameworks to solve the problems of ratings prediction and items ranking, respectively. The experimental results on three real-world datasets show that our proposed approaches achieve better results than the state-of-the-art MF with explicit social links (with 3.0% improvement on RMSE), and social BPR (with 9.1% improvement on AUC).

  4. Implicit frictional-contact model for soft particle systems

    Science.gov (United States)

    Nezamabadi, Saeid; Radjai, Farhang; Averseng, Julien; Delenne, Jean-Yves

    2015-10-01

    We introduce a novel numerical approach for the simulation of soft particles interacting via frictional contacts. This approach is based on an implicit formulation of the Material Point Method, allowing for large particle deformations, combined with the Contact Dynamics method for the treatment of unilateral frictional contacts between particles. This approach is both precise due to the treatment of contacts with no regularization and artificial damping parameters, and robust due to implicit time integration of both bulk degrees of freedom and relative contact velocities at the nodes representing the contact points. By construction, our algorithm is capable of handling arbitrary particle shapes and deformations. We illustrate this approach by two simple 2D examples: a Hertz contact and a rolling particle on an inclined plane. We also investigate the compaction of a packing of circular particles up to a solid fraction well above the jamming limit of hard particles. We find that, for the same level of deformation, the solid fraction in a packing of frictional particles is above that of a packing of frictionless particles as a result of larger particle shape change.

  5. Consistence beats causality in recommender systems

    CERN Document Server

    Zhu, Xuzhen; Hu, Zheng; Zhang, Ping; Zhou, Tao

    2015-01-01

    The explosive growth of information challenges people's capability in finding out items fitting to their own interests. Recommender systems provide an efficient solution by automatically push possibly relevant items to users according to their past preferences. Recommendation algorithms usually embody the causality from what having been collected to what should be recommended. In this article, we argue that in many cases, a user's interests are stable, and thus the previous and future preferences are highly consistent. The temporal order of collections then does not necessarily imply a causality relationship. We further propose a consistence-based algorithm that outperforms the state-of-the-art recommendation algorithms in disparate real data sets, including \\textit{Netflix}, \\textit{MovieLens}, \\textit{Amazon} and \\textit{Rate Your Music}.

  6. Exact Null Controllability for Fractional Nonlocal Integrodifferential Equations via Implicit Evolution System

    Directory of Open Access Journals (Sweden)

    Amar Debbouche

    2012-01-01

    Full Text Available We introduce a new concept called implicit evolution system to establish the existence results of mild and strong solutions of a class of fractional nonlocal nonlinear integrodifferential system, then we prove the exact null controllability result of a class of fractional evolution nonlocal integrodifferential control system in Banach space. As an application that illustrates the abstract results, two examples are provided.

  7. Recommender systems and the social web leveraging tagging data for recommender systems

    CERN Document Server

    Gedikli, Fatih

    2013-01-01

    There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user's individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of

  8. Electronic Resources Management System: Recommendation Report 2017

    KAUST Repository

    Ramli, Rindra M.

    2017-05-01

    This recommendation report provides an overview of the selection process for the new Electronic Resources Management System. The library has decided to move away from Innovative Interfaces Millennium ERM module. The library reviewed 3 system as potential replacements namely: Proquest 360 Resource Manager, Ex Libris Alma and Open Source CORAL ERMS. After comparing and trialling the systems, it was decided to go for Proquest 360 Resource Manager.

  9. Algorithms for Academic Search and Recommendation Systems

    DEFF Research Database (Denmark)

    Amolochitis, Emmanouil

    2014-01-01

    In this work we present novel algorithms for academic search, recommendation and association rules mining. In the first part of the work we introduce a novel hierarchical heuristic scheme for re-ranking academic publications. The scheme is based on the hierarchical combination of a custom...... are part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. In the third part of the work we present the design of a quantitative association rule mining algorithm. The introduced mining algorithm processes...... a specific number of user histories in order to generate a set of association rules with a minimally required support and confidence value. We have introduced a post processor that uses the generated association rules and improves the quality (in terms of recall) of the original recommendation functionality....

  10. Mining Feedback in Ranking and Recommendation Systems

    Science.gov (United States)

    Zhuang, Ziming

    2009-01-01

    The amount of online information has grown exponentially over the past few decades, and users become more and more dependent on ranking and recommendation systems to address their information seeking needs. The advance in information technologies has enabled users to provide feedback on the utilities of the underlying ranking and recommendation…

  11. User Controllability in a Hybrid Recommender System

    Science.gov (United States)

    Parra Santander, Denis Alejandro

    2013-01-01

    Since the introduction of Tapestry in 1990, research on recommender systems has traditionally focused on the development of algorithms whose goal is to increase the accuracy of predicting users' taste based on historical data. In the last decade, this research has diversified, with "human factors" being one area that has received…

  12. Behavior-based implicit planning method and its application to robot soccer system

    Institute of Scientific and Technical Information of China (English)

    Fan Changhong; Chen Weidong; Xi Yugeng

    2005-01-01

    A behavior-based implicit planning method is proposed through the design of a middle-size autonomous robot soccer system (MARSS). With basic goal-driven behaviors, the MARSS reactively selects suitable basic behavior according to different situation. The reactive executions of basic behavior sequences implicitly plan two primary behavior chains. By the robot's dynamical interactions with the environments, interleaving of basic behaviors spontaneously exhibits effective emergent behaviors to deal with some difficult situations. These emergent behaviors make the system simple, robust and competitive.

  13. Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model

    Directory of Open Access Journals (Sweden)

    Mojtaba Salehi

    2013-03-01

    Full Text Available In recent years, the explosion of learning materials in the web-based educational systems has caused difficulty of locating appropriate learning materials to learners. A personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable materials to learners. Since users express their opinions based on some specific attributes of items, this paper proposes a hybrid recommender system for learning materials based on their attributes to improve the accuracy and quality of recommendation. The presented system has two main modules: explicit attribute-based recommender and implicit attribute-based recommender. In the first module, weights of implicit or latent attributes of materials for learner are considered as chromosomes in genetic algorithm then this algorithm optimizes the weights according to historical rating. Then, recommendation is generated by Nearest Neighborhood Algorithm (NNA using the optimized weight vectors implicit attributes that represent the opinions of learners. In the second, preference matrix (PM is introduced that can model the interests of learner based on explicit attributes of learning materials in a multidimensional information model. Then, a new similarity measure between PMs is introduced and recommendations are generated by NNA. The experimental results show that our proposed method outperforms current algorithms on accuracy measures and can alleviate some problems such as cold-start and sparsity.

  14. Emoticon recommendation system for effective communication

    OpenAIRE

    Urabe, Yuki; Rzepka, Rafal; Araki, Kenji

    2013-01-01

    The existence of social media has made computer- mediated communication more widespread among users around the world. This paper describes the development of an emoticon recommendation system that allows users to express their feelings with their input. In order to develop this system, an innovative emoticon database consisting of a table of emoticons with points expressed from each of 10 distinctive emotions was constructed. An evaluation experiment showed that 71.3% of user-selected emotico...

  15. Adaptive information filtering for dynamic recommender systems

    CERN Document Server

    Jin, Ci-Hang; Zhang, Yi-Cheng; Zhou, Tao

    2009-01-01

    The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the proposed algorithms do not cumulate over time, that is to say, the global recomputing is unnecessary. This remarkable advantage is demonstrated by several metrics on algorithmic accuracy for two movie recommender systems and a social bookmarking system.

  16. Information transfer via implicit encoding with delay time modulation in a time-delay system

    Energy Technology Data Exchange (ETDEWEB)

    Kye, Won-Ho, E-mail: whkye@kipo.go.kr [Korean Intellectual Property Office, Government Complex Daejeon Building 4, 189, Cheongsa-ro, Seo-gu, Daejeon 302-701 (Korea, Republic of)

    2012-08-20

    A new encoding scheme for information transfer with modulated delay time in a time-delay system is proposed. In the scheme, the message is implicitly encoded into the modulated delay time. The information transfer rate as a function of encoding redundancy in various noise scales is presented and it is analyzed that the implicit encoding scheme (IES) has stronger resistance against channel noise than the explicit encoding scheme (EES). In addition, its advantages in terms of secure communication and feasible applications are discussed. -- Highlights: ► We propose new encoding scheme with delay time modulation. ► The message is implicitly encoded with modulated delay time. ► The proposed scheme shows stronger resistance against channel noise.

  17. Preference Based Personalized News Recommender System

    Directory of Open Access Journals (Sweden)

    Mansi Sood

    2014-06-01

    Full Text Available News reading has changed from the traditional model of hardcopy newspapers to online news access. Thousands of news sources are available on internet each having millions of articles to choose from, leaving users tangled to find out a relevant article that matches their interests and liking. Recommender Systems can be used as a solution to this information overload problem by identifying the interest areas of a user by creating user profiles, maintaining those profiles to keep accommodating changing user interests and presenting a set of recent news articles formed as recommendations based on those user profiles. This paper presents an algorithm, which requests one time input from users (during the signup about their preference of news categories (like Sports, Entertainment etc., which they would like to subscribe and creates a personalized profile for each user. Subsequently, it requests an optional feedback on the recommended articles, to intelligently update user profiles, and recommend relevant articles to them, based on their changing interests. The paper also presents a simulation of the proposed algorithm on various use cases to depict the correctness and robustness of the algorithm. Also, it gives a brief idea about implementation details and challenges associated with the algorithm.

  18. Hybrid Recommender System based on Autoencoders

    OpenAIRE

    Strub, Florian; Gaudel, Romaric; Mary, Jérémie

    2016-01-01

    International audience; A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach. In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing...

  19. Exact, approximate solutions and error bounds for coupled implicit systems of partial differential equations

    Directory of Open Access Journals (Sweden)

    Lucas Jódar

    1992-01-01

    Full Text Available In this paper coupled implicit initial-boundary value systems of second order partial differential equations are considered. Given a finite domain and an admissible error ϵ an analytic approximate solution whose error is upper bounded by ϵ in the given domain is constructed in terms of the data.

  20. Recommender System for Personalised Wellness Therapy

    Directory of Open Access Journals (Sweden)

    Thean Pheng Lim

    2013-10-01

    Full Text Available rising costs and risks in health care have shifted the preference of individuals from health treatment to disease prevention. This prevention treatment is known as wellness. In recent years, the Internet has become a popular place for wellness-conscious users to search for wellness-related information and solutions. As the user community becomes more wellness conscious, service improvement is needed to help users find relevant personalised wellness solutions. Due to rapid development in the wellness market, users value convenient access to wellness services. Most wellness websites reflect common health informatics approaches; these amount to more than 70,000 sites worldwide. Thus, the wellness industry should improve its Internet services in order to provide better and more convenient customer service. This paper discusses the development of a wellness recommender system that would help users find and adapt suitable personalised wellness therapy treatments based on their individual needs. This paper introduces new approaches that enhance the convenience and quality of wellness information delivery on the Internet. The wellness recommendation task is performed using an Artificial Intelligence technique of hybrid case-based reasoning (HCBR. HCBR solves users’ current wellness problems by applying solutions from similar cases in the past. From the evaluation results for our prototype wellness recommendation system, we conclude that wellness consultants are using consistent wellness knowledge to recommend solutions for sample wellness cases generated through an online consultation form. Thus, the proposed model can be integrated into wellness websites to enable users to search for suitable personalized wellness therapy treatment based on their health condition.

  1. Hybrid Recommender System for Joining Virtual Communities

    Directory of Open Access Journals (Sweden)

    Leila Esmaeili

    2012-03-01

    Full Text Available The variety of social networks and virtual communities has created problematic for users of different ages and preferences; in addition, since the true nature of groups is not clearly outlined, users are uncertain about joining various virtual groups and usually face the trouble of joining the undesired ones. As a solution, in this study, we introduced the hybrid community recommender system which offers customized recommendations based on user preferences. Although techniques such as content based filtering and collaborative filtering methods are available, these techniques are not enough efficient and in some cases make problems and bring limitations to users. Our method is based on a combination of content based filtering and collaborative filtering methods. It is created by selecting related features of users based on supervised entropy as well as using association rules and classification method. Supposing users in each community or group share similar characteristics, by hierarchical clustering, heterogeneous members are identified and removed. Unlike other methods, this is also applicable for users who have just joined the social network where they do not have any connections or group memberships. In such situations, this method could still offer recommendations.

  2. Cohesion Based Personalized Community Recommendation System

    Directory of Open Access Journals (Sweden)

    Md Mamunur Rashid

    2016-08-01

    Full Text Available Our life is totally engaged by the progressive growth of online social networking. Because, millions of users are interconnecting with each other using different social media sites like Facebook, Twitter, LinkedIn, Google+, Pinterest, Instagram etc. Most of the social sites like Facebook, Google+ allow users to join different groups or communities where people can share their common interests and express opinions around a common cause, problem or activity. However, an information overloading issue has disturbed users as thousands of communities or groups are creating each day. To resolve this problem, we have presented a community or group recommendation system centered on cohesion where cohesion represents high degree of connectedness among users in social network. In this paper, we emphasis on suggesting useful communities (or groups in term of Facebook that users personally attracted in to join; reducing the effort to find useful information based on cohesion. Our projected framework contains of the steps like: extracting sub-network from a social networking site (SNS, computing the impact of amity(both real-life or social and SNS connected, measuring user proclivity factor, calculating threshold from existing communities or groups of a user and lastly recommending community or group based on derived threshold. In result analysis part, we consider the precision-recall values by discarding community or group one at a time from the list of communities or groups of a certain user and checking whether the removed community or group is recommended by our proposed system. We have evaluated our system with 20 users and found 76% F1 accuracy measure.

  3. Recommendation Report: EJournals/EBooks A-Z Management System

    KAUST Repository

    Ramli, Rindra M.

    2014-01-01

    This is a recommendation report for KAUST Library on the Ejournals / EBooks AZ Management systems project. It briefly described the issues faced by the ERM Team, project plan overview and the project findings as well as the recommendation(s).

  4. Two tier pricing system recommendation and summary

    Energy Technology Data Exchange (ETDEWEB)

    None

    1974-06-01

    In both the U.S. and the world today the most critical price system problem is the spectacular crude price set by the OPEC monopoly. In the U.S. this $10.00 plus price currently sets the price of 60% of our crude petroleum input. Therefore, the most powerful method available to reduce U.S. crude input price inflation is to reduce the OPEC monopoly price by at least $2 or 20%. At this price, it would supposedly approximate the long run cost of such energy. The situation is reviewed and recommendations and a summary are provided.

  5. Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System

    OpenAIRE

    Yao, Feng; Yerima, Suleiman Y.; Kang, BooJoong; Sezer, Sakir

    2017-01-01

    As mobile devices have become indispensable in modern life, mobile security is becoming much more important. Traditional password or PIN-like point-of-entry security measures score low on usability and are vulnerable to brute force and other types of attacks. In order to improve mobile security, an adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication system is proposed in this paper to provide authentication in a continuous and transparent manner.To illustrate the applic...

  6. Recommender Systems by means of Information Retrieval

    CERN Document Server

    Costa, Alberto

    2010-01-01

    In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion (the so-called "leave-n-out approach"). In order to use an Information Retrieval algorithm, we reformulate this Recommender Systems problem in this way: a user corresponds to a document, a movie corresponds to a term, the active user (whose rating we want to predict) plays the role of the query, and the ratings are used as weigths, in place of the weighting schema of the original IR algorithm. The output is the ranking list of the documents ("users") relevant for the query ("active user"). We use the ratings of these users, weighted according to the rank, to predict the rating of the active user. We carry out the comparison by means of a typical metric, namely the accuracy of the predictions returned by the algorithm, and we...

  7. Implicit Sociology, Interdisciplinarity and Systems Theories in Agricultural Science

    NARCIS (Netherlands)

    Jansen, K.

    2009-01-01

    Recurring political and economic crises in agriculture lie behind policymakers' demands for more interdisciplinary, problem-solving approaches. This article examines different systems theories in agricultural sciences that claim to adopt interdisciplinarity and to bridge a supposed gap between the n

  8. On the System of Nonlinear Mixed Implicit Equilibrium Problems in Hilbert Spaces

    Directory of Open Access Journals (Sweden)

    Yeol Je Cho

    2010-01-01

    Full Text Available We use the Wiener-Hopf equations and the Yosida approximation notions to prove the existence theorem of a system of nonlinear mixed implicit equilibrium problems (SMIE in Hilbert spaces. The algorithm for finding a solution of the problem (SMIE is suggested; the convergence criteria and stability of the iterative algorithm are discussed. The results presented in this paper are more general and are viewed as an extension, refinement, and improvement of the previously known results in the literature.

  9. User Modeling for Personalized Recommender Systems

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    This paper models a user's interest and value related characteristics based on the extension by using neural networks and econometric techniques to the concept of user model in recommender systems. A two-dimensional taxonomy of user model was proposed in terms of the content and persistence of user characteristics. A user interest model and a customer lifetime value model were developed for the proposed taxonomy framework, to capture the time-dependent evolving nature of user's interests and his/her long-term profitability. The proposed models were empirically validated by using real customer data from a bee product company in health care industry. The experimental results show that these models provide effective assistant tools for the company to target its most valuable customers and implement one-to-one personalized services.

  10. Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword

    OpenAIRE

    Suka Parwita, Wayan Gede; Winarko, Edi

    2015-01-01

    AbstrakRecommendation system sering dibangun dengan memanfaatkan data peringkat item dan data identitas pengguna. Data peringkat item merupakan data yang langka pada sistem yang baru dibangun. Sedangkan, pemberian data identitas pada recommendation system dapat menimbulkan kekhawatiran penyalahgunaan data identitas.Hybrid recommendation system memanfaatkan algoritma penggalian frequent itemset dan perbandingan keyword dapat memberikan daftar rekomendasi tanpa menggunakan data identitas penggu...

  11. Enhancing practical multifunctional myoelectric applications through implicit motor control training systems.

    Science.gov (United States)

    Ison, Mark; Artemiadis, Panagiotis

    2014-01-01

    Despite holding promise for advances in prostheses and robot teleoperation, myoelectric controlled interfaces have had limited impact in commercial applications. Simultaneous multifunctional controls are desired, but often lead to frustration by users who cannot easily control the devices using state-of-the-art control schemes. This paper proposes and validates the use of implicit motor control training systems (IM-CTS) to achieve practical implementations of multifunctional myoelectric applications. Subjects implicitly develop muscle synergies needed to control a robotic application through an analogous visual interface without the associated physical constraints which may hinder learning. The learning then naturally transfers to perceived intuitive and robust control of the robotic device. The efficacy of the method is tested by comparing performance between two groups learning controls implicitly via the visual interface and explicitly via the robotic interface, respectively. The groups achieved comparable performance when performing tasks with the robotic device a week later. Moreover, the initial performance of the experimental group was significantly better than the control group achieved after up to 75 minutes of training. These findings support the use of IMCTS to achieve practical multifunctional control of a wide range of myoelectric applications without limiting them to intuitive mappings nor anthropomorphic devices.

  12. Recommendations

    Science.gov (United States)

    Brazelton, G. Blue; Renn, Kristen A.; Stewart, Dafina-Lazarus

    2015-01-01

    In this chapter, the editors provide a summary of the information shared in this sourcebook about the success of students who have minoritized identities of sexuality or gender and offer recommendations for policy, practice, and further research.

  13. Traffic management system: Recommendations. Final report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-09-30

    This report identifies the primary and secondary air traffic networks inside and outside Buenos Aires Metropolitan Area where particular safety and traffic problems exist. The Consortium Louis Berger International, Inc.-IBI Group-UBATEC provides recommendations divided into two groups: one based on engineering aspects for each identified deficiency in the selected routes; and a second group that is based on the results of the evaluations of needs. This is Volume 5, Recommendations Final Report, and it provides recommendations to optimize transportation in the city of Buenos Aires.

  14. Web based Multimedia Recommendation System for e-Learning Website

    Directory of Open Access Journals (Sweden)

    Syed Farhan Mohsin

    2010-01-01

    Full Text Available Recommendation systems are playing a vital role for an e-commerce business, which help to protect the customers from information overload. The visitors of such websites need guidance as well as recommendations through which they can find their desired results. Without recommendations it required extra time to get the result. This idea leads to the research on “Multimedia Recommendation System for e-learning website”. We proposed an Algorithm using Tag based content search technique, which provide the desired recommendations in the form of text, image, audio and video. In the first phase of the research the recommendations are coming from the same website only.

  15. Recommender systems for technology enhanced learning research trends and applications

    CERN Document Server

    Manouselis, Nikos; Verbert, Katrien

    2014-01-01

    Presents cutting edge research from leading experts in the growing field of Recommender Systems for Technology Enhanced Learning (RecSys TEL) International contributions are included to demonstrate the merging of various efforts and communities Topics include: Linked Data and the Social Web as Facilitators for TEL Recommender Systems in Research and Practice, Personalised Learning-Plan Recommendations in Game-Based Learning and Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem

  16. Recommendation strategies for e-learning: preliminary effects of a personal recommender system for lifelong learners

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Van den Berg, Bert; Eshuis, Jannes; Berlanga, Adriana; Nadolski, Rob; Waterink, Wim; Boers, Nanda; Koper, Rob

    2007-01-01

    Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Berlanga, A. J., Nadolski, R. J., Waterink, W., Boers, N., & Koper, R. (2007). Recommendation strategies for e-learning: preliminary effects of personal recommender system for lifelong learners. Unpublished manuscript.

  17. Recommendations for learners are different: Applying memory-based recommender system techniques to lifelong learning

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Koper, Rob

    2007-01-01

    Drachsler, H., Hummel, H. G. K., & Koper, R. (2007). Recommendations for learners are different: applying memory-based recommender system techniques to lifelong learning. Paper presented at the SIRTEL workshop at the EC-TEL 2007 Conference. September, 17-20, 2007, Crete, Greece.

  18. Do recommender systems benefit users? a modeling approach

    Science.gov (United States)

    Yeung, Chi Ho

    2016-04-01

    Recommender systems are present in many web applications to guide purchase choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products remains less explored. While in many cases the recommended products are relevant to users, in other cases customers may be tempted to purchase the products only because they are recommended. Here we introduce a model to examine the benefit of recommender systems for users, and find that recommendations from the system can be equivalent to random draws if one always follows the recommendations and seldom purchases according to his or her own preference. Nevertheless, with sufficient information about user preferences, recommendations become accurate and an abrupt transition to this accurate regime is observed for some of the studied algorithms. On the other hand, we find that high estimated accuracy indicated by common accuracy metrics is not necessarily equivalent to high real accuracy in matching users with products. This disagreement between estimated and real accuracy serves as an alarm for operators and researchers who evaluate recommender systems merely with accuracy metrics. We tested our model with a real dataset and observed similar behaviors. Finally, a recommendation approach with improved accuracy is suggested. These results imply that recommender systems can benefit users, but the more frequently a user purchases the recommended products, the less relevant the recommended products are in matching user taste.

  19. Achieving enlightenment: what do we know about the implicit learning system and its interaction with explicit knowledge?

    Science.gov (United States)

    Vidoni, Eric D; Boyd, Lara A

    2007-09-01

    Two major memory and learning systems operate in the brain: one for facts and ideas (ie, the declarative or explicit system), one for habits and behaviors (ie, the procedural or implicit system). Broadly speaking these two memory systems can operate either in concert or entirely independently of one another during the performance and learning of skilled motor behaviors. This Special Issue article has two parts. In the first, we present a review of implicit motor skill learning that is largely centered on the interactions between declarative and procedural learning and memory. Because distinct neuroanatomical substrates support unique aspects of learning and memory and thus focal injury can cause impairments that are dependent on lesion location, we also broadly consider which brain regions mediate implicit and explicit learning and memory. In the second part of this article, the interactive nature of these two memory systems is illustrated by the presentation of new data that reveal that both learning implicitly and acquiring explicit knowledge through physical practice lead to motor sequence learning. In our new data, we discovered that for healthy individuals use of the implicit versus explicit memory system differently affected variability of performance during acquisition practice; variability was higher early in practice for the implicit group and later in practice for the acquired explicit group. Despite the difference in performance variability, by retention both groups demonstrated comparable change in tracking accuracy and thus, motor sequence learning. Clinicians should be aware of the potential effects of implicit and explicit interactions when designing rehabilitation interventions, particularly when delivering explicit instructions before task practice, working with individuals with focal brain damage, and/or adjusting therapeutic parameters based on acquisition performance variability.

  20. Modeling mutual feedback between users and recommender systems

    CERN Document Server

    Zeng, An; Medo, Matus; Zhang, Yi-Cheng

    2015-01-01

    Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy.

  1. Collaborative Personalized Web Recommender System using Entropy based Similarity Measure

    CERN Document Server

    Mehta, Harita; Bedi, Punam; Dixit, V S

    2012-01-01

    On the internet, web surfers, in the search of information, always strive for recommendations. The solutions for generating recommendations become more difficult because of exponential increase in information domain day by day. In this paper, we have calculated entropy based similarity between users to achieve solution for scalability problem. Using this concept, we have implemented an online user based collaborative web recommender system. In this model based collaborative system, the user session is divided into two levels. Entropy is calculated at both the levels. It is shown that from the set of valuable recommenders obtained at level I; only those recommenders having lower entropy at level II than entropy at level I, served as trustworthy recommenders. Finally, top N recommendations are generated from such trustworthy recommenders for an online user.

  2. Semi-implicit integration factor methods on sparse grids for high-dimensional systems

    Science.gov (United States)

    Wang, Dongyong; Chen, Weitao; Nie, Qing

    2015-07-01

    Numerical methods for partial differential equations in high-dimensional spaces are often limited by the curse of dimensionality. Though the sparse grid technique, based on a one-dimensional hierarchical basis through tensor products, is popular for handling challenges such as those associated with spatial discretization, the stability conditions on time step size due to temporal discretization, such as those associated with high-order derivatives in space and stiff reactions, remain. Here, we incorporate the sparse grids with the implicit integration factor method (IIF) that is advantageous in terms of stability conditions for systems containing stiff reactions and diffusions. We combine IIF, in which the reaction is treated implicitly and the diffusion is treated explicitly and exactly, with various sparse grid techniques based on the finite element and finite difference methods and a multi-level combination approach. The overall method is found to be efficient in terms of both storage and computational time for solving a wide range of PDEs in high dimensions. In particular, the IIF with the sparse grid combination technique is flexible and effective in solving systems that may include cross-derivatives and non-constant diffusion coefficients. Extensive numerical simulations in both linear and nonlinear systems in high dimensions, along with applications of diffusive logistic equations and Fokker-Planck equations, demonstrate the accuracy, efficiency, and robustness of the new methods, indicating potential broad applications of the sparse grid-based integration factor method.

  3. A NEW HYBRID ALGORITHM FOR BUSINESS INTELLIGENCE RECOMMENDER SYSTEM

    Directory of Open Access Journals (Sweden)

    P.Prabhu

    2014-03-01

    Full Text Available Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful and useful information. Recommender system is one of business intelligence system that is used to obtain knowledge to the active user for better decision making. Recommender systems apply data mining techniques to the problem of making personalized recommendations for information. Due to the growth in the number of information and the users in recent years offers challenges in recommender systems. Collaborative, content, demographic and knowledge-based are four different types of recommendations systems. In this paper, a new hybrid algorithm is proposed for recommender system which combines knowledge based, profile of the users and most frequent item mining technique to obtain intelligence.

  4. Modelling Implicit Communication in Multi-Agent Systems with Hybrid Input/Output Automata

    Directory of Open Access Journals (Sweden)

    Marta Capiluppi

    2012-10-01

    Full Text Available We propose an extension of Hybrid I/O Automata (HIOAs to model agent systems and their implicit communication through perturbation of the environment, like localization of objects or radio signals diffusion and detection. To this end we decided to specialize some variables of the HIOAs whose values are functions both of time and space. We call them world variables. Basically they are treated similarly to the other variables of HIOAs, but they have the function of representing the interaction of each automaton with the surrounding environment, hence they can be output, input or internal variables. Since these special variables have the role of simulating implicit communication, their dynamics are specified both in time and space, because they model the perturbations induced by the agent to the environment, and the perturbations of the environment as perceived by the agent. Parallel composition of world variables is slightly different from parallel composition of the other variables, since their signals are summed. The theory is illustrated through a simple example of agents systems.

  5. Marketing Recommender Systems: A New Approach in Digital Economy

    Directory of Open Access Journals (Sweden)

    Loredana MOCEAN

    2012-01-01

    Full Text Available Marketing information systems are those systems which make the gathering, processing, selection, storage, transmission and display of coordinated and continuous internal and external information. Includes systematic and formal methods used for managing all of an organization's information market. Recommendation systems are those systems that are widely used in online systems to suggest items that users might find interesting. These recommendations are generated using in particular two techniques: content-based and collaborative filtering. This paper aims to define a new system, namely Marketing Recommender System, a system that serves marketing and uses techniques and methods of the digital economy.

  6. Promoting cold-start items in recommender systems

    CERN Document Server

    Liu, Jin-Hu; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min

    2014-01-01

    As one of major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, so-called the item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. To our surprise, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such ...

  7. Deployment of Recommender Systems: Operational and Strategic Issues

    Science.gov (United States)

    Ghoshal, Abhijeet

    2011-01-01

    E-commerce firms are increasingly adopting recommendation systems to effectively target customers with products and services. The first essay examines the impact that improving a recommender system has on firms that deploy such systems. A market with customers heterogeneous in their search costs is considered. We find that in a monopoly, a firm…

  8. Deployment of Recommender Systems: Operational and Strategic Issues

    Science.gov (United States)

    Ghoshal, Abhijeet

    2011-01-01

    E-commerce firms are increasingly adopting recommendation systems to effectively target customers with products and services. The first essay examines the impact that improving a recommender system has on firms that deploy such systems. A market with customers heterogeneous in their search costs is considered. We find that in a monopoly, a firm…

  9. Improving an Hybrid Literary Book Recommendation System through Author Ranking

    CERN Document Server

    Vaz, Paula Cristina; Martins, Bruno; Calado, Pavel

    2012-01-01

    Literary reading is an important activity for individuals and choosing to read a book can be a long time commitment, making book choice an important task for book lovers and public library users. In this paper we present an hybrid recommendation system to help readers decide which book to read next. We study book and author recommendation in an hybrid recommendation setting and test our approach in the LitRec data set. Our hybrid book recommendation approach purposed combines two item-based collaborative filtering algorithms to predict books and authors that the user will like. Author predictions are expanded in to a book list that is subsequently aggregated with the former list generated through the initial collaborative recommender. Finally, the resulting book list is used to yield the top-n book recommendations. By means of various experiments, we demonstrate that author recommendation can improve overall book recommendation.

  10. Diffusion-Based Recommendation in Collaborative Tagging Systems

    Institute of Scientific and Technical Information of China (English)

    SHANG Ming-Sheng; ZHANG Zi-Ke

    2009-01-01

    Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and therefore have the potential to help in improving better personalized recommendations. We propose a diffusion-based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset: Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.

  11. Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering

    CERN Document Server

    Nowakowski, Samuel; Boyer, Anne

    2010-01-01

    In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.

  12. Solving the stability-accuracy-diversity dilemma of recommender systems

    Science.gov (United States)

    Hou, Lei; Liu, Kecheng; Liu, Jianguo; Zhang, Runtong

    2017-02-01

    Recommender systems are of great significance in predicting the potential interesting items based on the target user's historical selections. However, the recommendation list for a specific user has been found changing vastly when the system changes, due to the unstable quantification of item similarities, which is defined as the recommendation stability problem. To improve the similarity stability and recommendation stability is crucial for the user experience enhancement and the better understanding of user interests. While the stability as well as accuracy of recommendation could be guaranteed by recommending only popular items, studies have been addressing the necessity of diversity which requires the system to recommend unpopular items. By ranking the similarities in terms of stability and considering only the most stable ones, we present a top- n-stability method based on the Heat Conduction algorithm (denoted as TNS-HC henceforth) for solving the stability-accuracy-diversity dilemma. Experiments on four benchmark data sets indicate that the TNS-HC algorithm could significantly improve the recommendation stability and accuracy simultaneously and still retain the high-diversity nature of the Heat Conduction algorithm. Furthermore, we compare the performance of the TNS-HC algorithm with a number of benchmark recommendation algorithms. The result suggests that the TNS-HC algorithm is more efficient in solving the stability-accuracy-diversity triple dilemma of recommender systems.

  13. Which recommender system can best fit social learning platforms?

    NARCIS (Netherlands)

    Fazeli, Soude; Loni, Babak; Drachsler, Hendrik; Sloep, Peter

    2014-01-01

    In this presentation, we present a study that aims to develop a recommender system for social learning platforms that combine traditional learning management systems with commercial social networks like Facebook. We therefore take into account social interactions of users to make recommendations on

  14. Which Recommender System Can Best Fit Social Learning Platforms?

    NARCIS (Netherlands)

    Fazeli, Soude; Loni, Babak; Drachsler, Hendrik; Sloep, Peter

    2014-01-01

    This study aims to develop a recommender system for social learning platforms that combine traditional learning management systems with commercial social networks like Facebook. We therefore take into account social interactions of users to make recommendations on learning resources. We propose to m

  15. Finding Your Literature Match -- A Recommender System

    CERN Document Server

    Henneken, Edwin A; Accomazzi, Alberto; Grant, Carolyn; Thompson, Donna; Bohlen, Elizabeth; Di Milia, Giovanni; Luker, Jay; Murray, Stephen S

    2010-01-01

    The universe of potentially interesting, searchable literature is expanding continuously. Besides the normal expansion, there is an additional influx of literature because of interdisciplinary boundaries becoming more and more diffuse. Hence, the need for accurate, efficient and intelligent search tools is bigger than ever. Even with a sophisticated search engine, looking for information can still result in overwhelming results. An overload of information has the intrinsic danger of scaring visitors away, and any organization, for-profit or not-for-profit, in the business of providing scholarly information wants to capture and keep the attention of its target audience. Publishers and search engine engineers alike will benefit from a service that is able to provide visitors with recommendations that closely meet their interests. Providing visitors with special deals, new options and highlights may be interesting to a certain degree, but what makes more sense (especially from a commercial point of view) than to...

  16. Performing Hybrid Recommendation in Intermodal Transportation-the FTMarket System's Recommendation Module

    CERN Document Server

    Lazanas, Alexis

    2009-01-01

    Diverse recommendation techniques have been already proposed and encapsulated into several e-business applications, aiming to perform a more accurate evaluation of the existing information and accordingly augment the assistance provided to the users involved. This paper reports on the development and integration of a recommendation module in an agent-based transportation transactions management system. The module is built according to a novel hybrid recommendation technique, which combines the advantages of collaborative filtering and knowledge-based approaches. The proposed technique and supporting module assist customers in considering in detail alternative transportation transactions that satisfy their requests, as well as in evaluating completed transactions. The related services are invoked through a software agent that constructs the appropriate knowledge rules and performs a synthesis of the recommendation policy.

  17. TDCCREC: AN EFFICIENT AND SCALABLE WEB-BASED RECOMMENDATION SYSTEM

    Directory of Open Access Journals (Sweden)

    K.Latha

    2010-10-01

    Full Text Available Web browsers are provided with complex information space where the volume of information available to them is huge. There comes the Recommender system which effectively recommends web pages that are related to the current webpage, to provide the user with further customized reading material. To enhance the performance of the recommender systems, we include an elegant proposed web based recommendation system; Truth Discovery based Content and Collaborative RECommender (TDCCREC which is capable of addressing scalability. Existing approaches such as Learning automata deals with usage and navigational patterns of users. On the other hand, Weighted Association Rule is applied for recommending web pages by assigning weights to each page in all the transactions. Both of them have their own disadvantages. The websites recommended by the search engines have no guarantee for information correctness and often delivers conflicting information. To solve them, content based filtering and collaborative filtering techniques are introduced for recommending web pages to the active user along with the trustworthiness of the website and confidence of facts which outperforms the existing methods. Our results show how the proposed recommender system performs better in predicting the next request of web users.

  18. Intelligent recommendation system for e-learning platforms

    OpenAIRE

    Tavares, Bruno; Faria, Luiz; Martins, Constantino

    2012-01-01

    As more and more digital resources are available, finding the appropriate document becomes harder. Thus, a new kind of tools, able to recommend the more appropriated resources according the user needs, becomes even more necessary. The current project implements an intelligent recommendation system for elearning platforms. The recommendations are based on one hand, the performance of the user during the training process and on the other hand, the requests made by the user in the fo...

  19. Integrating Information Extraction Agents into a Tourism Recommender System

    Science.gov (United States)

    Esparcia, Sergio; Sánchez-Anguix, Víctor; Argente, Estefanía; García-Fornes, Ana; Julián, Vicente

    Recommender systems face some problems. On the one hand information needs to be maintained updated, which can result in a costly task if it is not performed automatically. On the other hand, it may be interesting to include third party services in the recommendation since they improve its quality. In this paper, we present an add-on for the Social-Net Tourism Recommender System that uses information extraction and natural language processing techniques in order to automatically extract and classify information from the Web. Its goal is to maintain the system updated and obtain information about third party services that are not offered by service providers inside the system.

  20. Recommended Practice for Securing Control System Modems

    Energy Technology Data Exchange (ETDEWEB)

    James R. Davidson; Jason L. Wright

    2008-01-01

    This paper addresses an often overlooked “backdoor” into critical infrastructure control systems created by modem connections. A modem’s connection to the public telephone system is similar to a corporate network connection to the Internet. By tracing typical attack paths into the system, this paper provides the reader with an analysis of the problem and then guides the reader through methods to evaluate existing modem security. Following the analysis, a series of methods for securing modems is provided. These methods are correlated to well-known networking security methods.

  1. Private personalized social recommendations in an IPTV system

    Science.gov (United States)

    Elmisery, Ahmed M.

    2014-04-01

    In our connected world, recommender systems have become widely known for their ability to provide expert and personalize referrals to end-users in different domains. The rapid growth of social networks and new kinds of systems so called "social recommender systems" are rising, where recommender systems can be utilized to find a suitable content according to end-users' personal preferences. However, preserving end-users' privacy in social recommender systems is a very challenging problem that might prevent end-users from releasing their own data, which detains the accuracy of extracted referrals. In order to gain accurate referrals, social recommender systems should have the ability to preserve the privacy of end-users registered in this system. In this paper, we present a middleware that runs on end-users' Set-top boxes to conceal their profile data when released for generating referrals, such that computation of recommendation proceeds over the concealed data. The proposed middleware is equipped with two concealment protocols to give users a complete control on the privacy level of their profiles. We present an IPTV network scenario and perform a number of different experiments to test the efficiency and accuracy of our protocols. As supported by the experiments, our protocols maintain the recommendations accuracy with acceptable privacy level.

  2. A fuzzy recommendation system for daily water intake

    Directory of Open Access Journals (Sweden)

    Bin Dai

    2016-05-01

    Full Text Available Water is one of the most important constituents of the human body. Daily consumption of water is thus necessary to protect human health. Daily water consumption is related to several factors such as age, ambient temperature, and degree of physical activity. These factors are generally difficult to express with exact numerical values. The main objective of this article is to build a daily water intake recommendation system using fuzzy methods. This system will use age, physical activity, and ambient temperature as the input factors and daily water intake values as the output factor. The reasoning mechanism of the fuzzy system can calculate the recommended value of daily water intake. Finally, the system will compare the actual recommended values with our system to determine the usefulness. The experimental results show that this recommendation system is effective in actual application.

  3. Precomputed Clustering for Movie Recommendation System in Real Time

    Directory of Open Access Journals (Sweden)

    Bo Li

    2014-01-01

    of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc. and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.

  4. Contexts in a Paper Recommendation System with Collaborative Filtering

    Science.gov (United States)

    Winoto, Pinata; Tang, Tiffany Ya; McCalla, Gordon

    2012-01-01

    Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users' interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students…

  5. Information Retrieval and User-Centric Recommender System Evaluation

    NARCIS (Netherlands)

    Said, A.; Bellogín Kouki, A.; Vries, A.P. de; Kille, B.

    2013-01-01

    Traditional recommender system evaluation focuses on raising the accuracy, or lowering the rating prediction error of the recommendation algorithm. Recently, however, discrepancies between commonly used metrics (e.g. precision, recall, root-mean-square error) and the experienced quality from the use

  6. Privacy-preserving content-based recommender system

    NARCIS (Netherlands)

    Erkin, Z.; Beye, M.; Veugen, T.; Lagendijk, R.L.

    2012-01-01

    By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing r

  7. Privacy-Preserving Content-Based Recommender System

    NARCIS (Netherlands)

    Erkin, Z.; Beye, M.; Veugen, P.J.M.; Lagendijk, R.L.

    2012-01-01

    By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing r

  8. Extending a Hybrid Tag-Based Recommender System with Personalization

    DEFF Research Database (Denmark)

    Durao, Frederico; Dolog, Peter

    2010-01-01

    extension for a hybrid tag-based recommender system, which suggests similar Web pages based on the similarity of their tags. The semantic extension aims at discovering tag relations which are not considered in basic syntax similarity. With the goal of generating more semantically grounded recommendations...

  9. An Ontology Based Approach to Implement the Online Recommendation System

    Directory of Open Access Journals (Sweden)

    Vijayakumar Mohanraj

    2011-01-01

    Full Text Available Problem statement: Every web user has different intent when accessing the information on website. The primary goal of recommendation system is to anticipate the user intent and recommend the web pages that contain user expected information. Effective recommendation of web pages involves two important challenges: accurately identifying the user intent and predict the result show that novel web usage mining method and ontological concept scoring algorithm based on website domain ontological profile helps the recommendation system imminent navigation pattern in such a way that it provides required content while users browse the predicted navigation. Approach: We present a ontology based approach to implement recommendation system that involves applying innovative web usage mining on log system to discover all possible imminent navigation patterns of current user and resolve any uncertainties in discovering the navigation pattern by applying ontological concept based similarity comparison and scoring algorithm. Results: result show that novel web usage mining method and ontological concept scoring algorithm based on website domain ontological profile helps the recommendation system to predict and present most relevant navigation pattern to users. Conclusion: our recommendation system confirms that ontology based approach should be used to ensure excellent accuracy in predicting and capturing future navigation pattern of web user.

  10. Promoting cold-start items in recommender systems.

    Science.gov (United States)

    Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min

    2014-01-01

    As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.

  11. Promoting Cold-Start Items in Recommender Systems

    Science.gov (United States)

    Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min

    2014-01-01

    As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs. PMID:25479013

  12. Random Graphs for Performance Evaluation of Recommender Systems

    CERN Document Server

    Chojnacki, Szymon

    2010-01-01

    The purpose of this article is to introduce a new analytical framework dedicated to measuring performance of recommender systems. The standard approach is to assess the quality of a system by means of accuracy related statistics. However, the specificity of the environments in which recommender systems are deployed requires to pay much attention to speed and memory requirements of the algorithms. Unfortunately, it is implausible to assess accurately the complexity of various algorithms with formal tools. This can be attributed to the fact that such analyses are usually based on an assumption of dense representation of underlying data structures. Whereas, in real life the algorithms operate on sparse data and are implemented with collections dedicated for them. Therefore, we propose to measure the complexity of recommender systems with artificial datasets that posses real-life properties. We utilize recently developed bipartite graph generator to evaluate how state-of-the-art recommender systems' behavior is d...

  13. A Transitive Recommendation System for Information Technology Communities

    Directory of Open Access Journals (Sweden)

    Hesham Ali

    2013-06-01

    Full Text Available Social networks have become a new trend for research among computer scientist around the world. Social network had an impact on users' way of life. One of social network usages is recommendation systems. The need of recommendation systems is arising when users try to know best choice for them in many items types (books, experts, locations, technologies...etc. The problem is that a single person can't try all alternatives in all possibles life goals to compare. Thus, a person has to use his friends' expertise to select better option in any item category. This process is the main idea of “Recommendation Systems”. Recommendation systems usually depend on users-to-items ratings in a network (graph. Two main challenges for recommendation systems are accuracy of recommendation and computation size. The main objective of this paper is to introduce a suggested technique for transitive recommendation system based on users' collaborative ratings, and also to balance loading of computation. All this has to be applied on a special type of social network. Our work studied the transitivity usage in connections to get a relation (path as a recommendation for nodes not directly connected. The target social network has eight types of nodes. So, there are techniques that are not suitable to this complex type of network. Those we can present a new support for recommending items of several types to users with several types. We believe that this functionality hasn't been fully provided elsewhere. We have suggested using single source shortest path algorithm combined with Map Reduce technique, and mathematically deduced that we have a speeding up of algorithm by 10% approximately. Our testing results shows an accuracy of 89% and false rejection of 99% compared to traditional algorithms with less configuration parameters and more steady count of recommendations.

  14. Do health care providers' attitudes towards back pain predict their treatment recommendations? Differential predictive validity of implicit and explicit attitude measures

    NARCIS (Netherlands)

    Houben, R.M.A.; Gijsen, A.; Peterson, J.; de Jong, P.J.; Vlaeyen, J.W.S.

    The current study aimed to measure the differential predictive value of implicit and explicit attitude measures on treatment behaviour of health care providers. Thirty-six physiotherapy students completed a measure of explicit treatment attitude (Pain Attitudes And Beliefs Scale For

  15. Do health care providers' attitudes towards back pain predict their treatment recommendations? Differential predictive validity of implicit and explicit attitude measures

    NARCIS (Netherlands)

    Houben, R.M.A.; Gijsen, A.; Peterson, J.; de Jong, P.J.; Vlaeyen, J.W.S.

    2005-01-01

    The current study aimed to measure the differential predictive value of implicit and explicit attitude measures on treatment behaviour of health care providers. Thirty-six physiotherapy students completed a measure of explicit treatment attitude (Pain Attitudes And Beliefs Scale For Physiotherapists

  16. A Top-N Recommender System Evaluation Protocol Inspired by Deployed Systems

    NARCIS (Netherlands)

    Said, A.; Bellogín Kouki, A.; Vries, A.P. de

    2013-01-01

    The evaluation of recommender systems is crucial for their development. In today's recommendation landscape there are many standardized recommendation algorithms and approaches, however, there exists no standardized method for experimental setup of evaluation -- not even for widely used measures suc

  17. Implicit CAPTCHAs

    Science.gov (United States)

    Baird, Henry S.; Bentley, Jon L.

    2005-01-01

    We propose a design methodology for "implicit" CAPTCHAs to relieve drawbacks of present technology. CAPTCHAs are tests administered automatically over networks that can distinguish between people and machines and thus protect web services from abuse by programs masquerading as human users. All existing CAPTCHAs' challenges require a significant conscious effort by the person answering them -- e.g. reading and typing a nonsense word -- whereas implicit CAPTCHAs may require as little as a single click. Many CAPTCHAs distract and interrupt users, since the challenge is perceived as an irrelevant intrusion; implicit CAPTCHAs can be woven into the expected sequence of browsing using cues tailored to the site. Most existing CAPTCHAs are vulnerable to "farming-out" attacks in which challenges are passed to a networked community of human readers; by contrast, implicit CAPTCHAs are not "fungible" (in the sense of easily answerable in isolation) since they are meaningful only in the specific context of the website that is protected. Many existing CAPTCHAs irritate or threaten users since they are obviously tests of skill: implicit CAPTCHAs appear to be elementary and inevitable acts of browsing. It can often be difficult to detect when CAPTCHAs are under attack: implicit CAPTCHAs can be designed so that certain failure modes are correlated with failed bot attacks. We illustrate these design principles with examples.

  18. A Personalized Tag-Based Recommendation in Social Web Systems

    DEFF Research Database (Denmark)

    Durao, Frederico; Dolog, Peter

    2009-01-01

    Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag...... to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us , a social bookmarking web system on which users can share their personal bookmarks...

  19. Lime and fertilizer recommendation system for coconut trees

    Directory of Open Access Journals (Sweden)

    Gustavo Nogueira Guedes Pereira Rosa

    2011-02-01

    Full Text Available Fertilizer recommendation to most agricultural crops is based on response curves. Such curves are constructed from field experimental data, obtained for a particular condition and may not be reliable to be applied to other regions. The aim of this study was to develop a Lime and Fertilizer Recommendation System for Coconut Crop based on the nutritional balance. The System considers the expected productivity and plant nutrient use efficiency to estimate nutrient demand, and effective rooting layer, soil nutrient availability, as well as any other nutrient input to estimate the nutrient supply. Comparing the nutrient demand with the nutrient supply the System defines the nutrient balance. If the balance for a given nutrient is negative, lime and, or, fertilization is recommended. On the other hand, if the balance is positive, no lime or fertilizer is needed. For coconut trees, the fertilization regime is divided in three stages: fertilization at the planting spot, band fertilization and fertilization at the production phase. The data set for the development of the System for coconut trees was obtained from the literature. The recommendations generated by the System were compared to those derived from recommendation tables used for coconut crop in Brazil. The main differences between the two procedures were for the P rate applied in the planting hole, which was higher in the proposed System because the tables do not pay heed to the pit volume, whereas the N and K rates were lower. The crop demand for K is very high, and the rates recommended by the System are superior to the table recommendations for the formation and initial production stage. The fertilizer recommendations by the System are higher for the phase of coconut tree growth as compared to the production phase, because greater amount of biomass is produced in the first phase.

  20. Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

    OpenAIRE

    Zhong-Da Tian; Shu-Jiang Li; Yan-Hong Wang; Hong-Xia Yu

    2015-01-01

    The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC). The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support...

  1. A Conceptual Framework for Evolving, Recommender Online Learning Systems

    Science.gov (United States)

    Peiris, K. Dharini Amitha; Gallupe, R. Brent

    2012-01-01

    A comprehensive conceptual framework is developed and described for evolving recommender-driven online learning systems (ROLS). This framework describes how such systems can support students, course authors, course instructors, systems administrators, and policy makers in developing and using these ROLS. The design science information systems…

  2. FERTILIZER RECOMMENDATION SYSTEM FOR MELON BASED ON NUTRITIONAL BALANCE

    Directory of Open Access Journals (Sweden)

    José Aridiano Lima de Deus

    2015-04-01

    Full Text Available Melon is one of the most demanding cucurbits regarding fertilization, requiring knowledge of soils, crop nutritional requirements, time of application, and nutrient use efficiency for proper fertilization. Developing support systems for decision-making for fertilization that considers these variables in nutrient requirement and supply is necessary. The objective of this study was parameterization of a fertilizer recommendation system for melon (Ferticalc-melon based on nutritional balance. To estimate fertilizer recommendation, the system considers the requirement subsystem (REQ, which includes the demand for nutrients by the plant, and the supply subsystem (SUP, which corresponds to the supply of nutrients through the soil and irrigation water. After determining the REQtotal and SUPtotal, the system calculates the nutrient balances for N, P, K, Ca, Mg, and S, recommending fertilizer application if the balance is negative (SUP < REQ, but not if the balance is positive or zero (SUP ≥ REQ. Simulations were made for different melon types (Yellow, Cantaloupe, Galia and Piel-de-sapo, with expected yield of 45 t ha-1. The system estimated that Galia type was the least demanding in P, while Piel-de-sapo was the most demanding. Cantaloupe was the least demanding for N and Ca, while the Yellow type required less K, Mg, and S. As compared to other fertilizer recommendation methods adopted in Brazil, the Ferticalc system was more dynamic and flexible. Although the system has shown satisfactory results, it needs to be evaluated under field conditions to improve its recommendations.

  3. Recommender System for Sales at Material Store Using Fuzzy Tsukamoto

    Directory of Open Access Journals (Sweden)

    July Kurniawan

    2016-02-01

    Full Text Available The retail business has developed very rapidly, especially in Indonesia. One of them is material stores that have not applied the technology and still manual. In this modern era of buying and selling consumers need systems to assist in overcoming problems in terms of recommend items based on customer needs. The aim of this study is to determine the needs of consumers to recommend the necessary consumer goods. This system will simplify these processes, by utilizing information technology using Tsukamoto fuzzy logic. So that consumer demand for faster and more accurate in recommending goods could be accommodated. This research outlines what is needed to overcome the problems that had been experienced by consumers with a lack of information. The recommendations of this study is the form that refers to the percentage of goods from the predictions that have been studied previously.

  4. A Multiagent Recommender System with Task-Based Agent Specialization

    Science.gov (United States)

    Lorenzi, Fabiana; Correa, Fabio Arreguy Camargo; Bazzan, Ana L. C.; Abel, Mara; Ricci, Francesco

    This paper describes a multiagent recommender system where agents maintain local knowledge bases and, when requested to support a travel planning task, they collaborate exchanging information stored in their local bases. A request for a travel recommendation is decomposed by the system into sub tasks, corresponding to travel services. Agents select tasks autonomously, and accomplish them with the help of the knowledge derived from previous solutions. In the proposed architecture, agents become experts in some task types, and this makes the recommendation generation more efficient. In this paper, we validate the model via simulations where agents collaborate to recommend a travel package to the user. The experiments show that specialization is useful hence providing a validation of the proposed model.

  5. Hybrid Recommender System Based on Personal Behavior Mining

    OpenAIRE

    Fang, Zhiyuan; Zhang, Lingqi; Chen, Kun

    2016-01-01

    Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied in Amazon, matrix factorization based collaborative filtering algorithm from Netflix, etc. In this article, we hope to combine traditional model with behavior pattern extraction method. We use desensitized mobile transaction record provided by T-mall, Alibaba to build a hybrid ...

  6. A Personalized Tag-Based Recommendation in Social Web Systems

    DEFF Research Database (Denmark)

    Durao, Frederico; Dolog, Peter

    2009-01-01

    -based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order...... to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us , a social bookmarking web system on which users can share their personal bookmarks...

  7. World Automata: a compositional approach to model implicit communication in hierarchical Hybrid Systems

    Directory of Open Access Journals (Sweden)

    Marta Capiluppi

    2013-08-01

    Full Text Available We propose an extension of Hybrid I/O Automata (HIOAs to model agent systems and their implicit communication through perturbation of the environment, like localization of objects or radio signals diffusion and detection. The new object, called World Automaton (WA, is built in such a way to preserve as much as possible of the compositional properties of HIOAs and its underlying theory. From the formal point of view we enrich classical HIOAs with a set of world variables whose values are functions both of time and space. World variables are treated similarly to local variables of HIOAs, except in parallel composition, where the perturbations produced by world variables are summed. In such way, we obtain a structure able to model both agents and environments, thus inducing a hierarchy in the model and leading to the introduction of a new operator. Indeed this operator, called inplacement, is needed to represent the possibility of an object (WA of living inside another object/environment (WA.

  8. Privacy-preserving recommender systems in dynamic environments

    NARCIS (Netherlands)

    Erkin, Z.; Veugen, T.; Lagendijk, R.L.

    2013-01-01

    Recommender systems play a crucial role today in on-line applications as they improve the customer satisfaction, and at the same time results in an increase in the profit for the service provider. However, there are serious privacy concerns as such systems rely on the personal data of the customers.

  9. Evaluation of Neighbourhood Selection Methods in Decentralized Recommendation Systems

    NARCIS (Netherlands)

    Clements, M.; Vries, A.P. de; Pouwelse, J.A.; Wang, J.; Reinders, M.J.T.

    2007-01-01

    Recommendation systems are important in social networks that allow the injection of user-generated content and let users indicate their preferences towards the content introduced by others. Considering the increase of usage of these collaborative systems, it seems only a matter of time before the cu

  10. Personal_Movie - A Geolocated Movie Recommendation System For Events

    Directory of Open Access Journals (Sweden)

    CAZELLA, S. C.

    2012-12-01

    Full Text Available Considering how hard it is to provide more assertive and personalized information, products and service for people/tourists who are searching for a service, such as: having lunch/dinner, searching what's hot about films in theaters right now in the "Olympic villa", for instance. In order to fill this gap this paper describes a Recommendation System (RS that applies contextual information and people' personality as recommender inputs in order to predict more personalized films for Cinemark's clients (Personal_Movie. In order to illustrate our discussion we present an experiment that uses a software for mobile that uses geo-location and people's personality to further improve the quality of the film recommendation. The experiment has shown promising results and its potential in the generation of more assertive recommendation. We believe the results might by asl applicable for other products and services requested in Brazilian mega events

  11. A Decision Fusion Framework for Treatment Recommendation Systems.

    Science.gov (United States)

    Mei, Jing; Liu, Haifeng; Li, Xiang; Xie, Guotong; Yu, Yiqin

    2015-01-01

    Treatment recommendation is a nontrivial task--it requires not only domain knowledge from evidence-based medicine, but also data insights from descriptive, predictive and prescriptive analysis. A single treatment recommendation system is usually trained or modeled with a limited (size or quality) source. This paper proposes a decision fusion framework, combining both knowledge-driven and data-driven decision engines for treatment recommendation. End users (e.g. using the clinician workstation or mobile apps) could have a comprehensive view of various engines' opinions, as well as the final decision after fusion. For implementation, we leverage several well-known fusion algorithms, such as decision templates and meta classifiers (of logistic and SVM, etc.). Using an outcome-driven evaluation metric, we compare the fusion engine with base engines, and our experimental results show that decision fusion is a promising way towards a more valuable treatment recommendation.

  12. TrustRank: a Cold-Start tolerant recommender system

    Science.gov (United States)

    Zou, Haitao; Gong, Zhiguo; Zhang, Nan; Zhao, Wei; Guo, Jingzhi

    2015-02-01

    The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. User- and item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust network to facilitate Cold-Start users is that these users also tend to have a very limited number of trust relationships. To address this challenge, we propose a pre-processing propagation of the Cold-Start users' trust network. In particular, by applying the personalised PageRank algorithm, we expand the friends of a given user to include others with similar purchase records to his/her original friends. To make this propagation algorithm scalable to a large amount of users, as required by real-world recommender systems, we devise an iterative computation algorithm of the original personalised TrustRank which can incrementally compute trust vectors for Cold-Start users. We conduct extensive experiments to demonstrate the consistently improvement provided by our proposed algorithm over the existing recommender algorithms on the accuracy of recommendations for Cold-Start users.

  13. Recruitment recommendation system based on fuzzy measure and indeterminate integral

    Science.gov (United States)

    Yin, Xin; Song, Jinjie

    2017-08-01

    In this study, we propose a comprehensive evaluation approach based on indeterminate integral. By introducing the related concepts of indeterminate integral and their formulas into the recruitment recommendation system, we can calculate the suitability of each job for different applicants with the defined importance for each criterion listed in the job advertisements, the association between different criteria and subjective assessment as the prerequisite. Thus we can make recommendations to the applicants based on the score of the suitability of each job from high to low. In the end, we will exemplify the usefulness and practicality of this system with samples.

  14. Vote Stuffing Control in IPTV-based Recommender Systems

    Science.gov (United States)

    Bhatt, Rajen

    Vote stuffing is a general problem in the functioning of the content rating-based recommender systems. Currently IPTV viewers browse various contents based on the program ratings. In this paper, we propose a fuzzy clustering-based approach to remove the effects of vote stuffing and consider only the genuine ratings for the programs over multiple genres. The approach requires only one authentic rating, which is generally available from recommendation system administrators or program broadcasters. The entire process is automated using fuzzy c-means clustering. Computational experiments performed over one real-world program rating database shows that the proposed approach is very efficient for controlling vote stuffing.

  15. GitHub open source project recommendation system

    OpenAIRE

    Matek, Tadej; Zebec, Svit Timej

    2016-01-01

    Hosting platforms for software projects can form collaborative social networks and a prime example of this is GitHub which is arguably the most popular platform of this kind. An open source project recommendation system could be a major feature for a platform like GitHub, enabling its users to find relevant projects in a fast and simple manner. We perform network analysis on a constructed graph based on GitHub data and present a recommendation system that uses link prediction.

  16. Anti-folksonomical Recommender System for Social Bookmarking Service

    Science.gov (United States)

    Sasaki, Akira; Miyata, Takamichi; Inazumi, Yasuhiro; Kobayashi, Aki; Sakai, Yoshinori

    Social bookmarking has been in the spotlight recently. Social bookmarking allows users to add several keywords called tags to items they bookmarked. Many previous works on social bookmarking using actual words for tags, called folksonomy, have come out. However, essential information of tags is in the classification of items by tags. Based on this assumption, we propose an anti-folksonomical recommendation system for calculating similarities between groups of items classified according to tags. In addition, we use hypothesis testing to improve these similarities based on statistical reliability. The experimental results show that our proposed system provides an appropriate recommendation result even if users tagged with different keywords.

  17. Inductive reasoning and implicit memory: evidence from intact and impaired memory systems.

    Science.gov (United States)

    Girelli, Luisa; Semenza, Carlo; Delazer, Margarete

    2004-01-01

    In this study, we modified a classic problem solving task, number series completion, in order to explore the contribution of implicit memory to inductive reasoning. Participants were required to complete number series sharing the same underlying algorithm (e.g., +2), differing in both constituent elements (e.g., 2468 versus 57911) and correct answers (e.g., 10 versus 13). In Experiment 1, reliable priming effects emerged, whether primes and targets were separated by four or ten fillers. Experiment 2 provided direct evidence that the observed facilitation arises at central stages of problem solving, namely the identification of the algorithm and its subsequent extrapolation. The observation of analogous priming effects in a severely amnesic patient strongly supports the hypothesis that the facilitation in number series completion was largely determined by implicit memory processes. These findings demonstrate that the influence of implicit processes extends to higher level cognitive domain such as induction reasoning.

  18. Health Recommender Systems: Concepts, Requirements, Technical Basics and Challenges

    Directory of Open Access Journals (Sweden)

    Martin Wiesner

    2014-03-01

    Full Text Available During the last decades huge amounts of data have been collected in clinical databases representing patients’ health states (e.g., as laboratory results, treatment plans, medical reports. Hence, digital information available for patient-oriented decision making has increased drastically but is often scattered across different sites. As as solution, personal health record systems (PHRS are meant to centralize an individual’s health data and to allow access for the owner as well as for authorized health professionals. Yet, expert-oriented language, complex interrelations of medical facts and information overload in general pose major obstacles for patients to understand their own record and to draw adequate conclusions. In this context, recommender systems may supply patients with additional laymen-friendly information helping to better comprehend their health status as represented by their record. However, such systems must be adapted to cope with the specific requirements in the health domain in order to deliver highly relevant information for patients. They are referred to as health recommender systems (HRS. In this article we give an introduction to health recommender systems and explain why they are a useful enhancement to PHR solutions. Basic concepts and scenarios are discussed and a first implementation is presented. In addition, we outline an evaluation approach for such a system, which is supported by medical experts. The construction of a test collection for case-related recommendations is described. Finally, challenges and open issues are discussed.

  19. Health recommender systems: concepts, requirements, technical basics and challenges.

    Science.gov (United States)

    Wiesner, Martin; Pfeifer, Daniel

    2014-03-03

    During the last decades huge amounts of data have been collected in clinical databases representing patients' health states (e.g., as laboratory results, treatment plans, medical reports). Hence, digital information available for patient-oriented decision making has increased drastically but is often scattered across different sites. As as solution, personal health record systems (PHRS) are meant to centralize an individual's health data and to allow access for the owner as well as for authorized health professionals. Yet, expert-oriented language, complex interrelations of medical facts and information overload in general pose major obstacles for patients to understand their own record and to draw adequate conclusions. In this context, recommender systems may supply patients with additional laymen-friendly information helping to better comprehend their health status as represented by their record. However, such systems must be adapted to cope with the specific requirements in the health domain in order to deliver highly relevant information for patients. They are referred to as health recommender systems (HRS). In this article we give an introduction to health recommender systems and explain why they are a useful enhancement to PHR solutions. Basic concepts and scenarios are discussed and a first implementation is presented. In addition, we outline an evaluation approach for such a system, which is supported by medical experts. The construction of a test collection for case-related recommendations is described. Finally, challenges and open issues are discussed.

  20. Recommendation systems in the scope of opinion formation: a model

    CERN Document Server

    Blattner, Marcel

    2012-01-01

    Aggregated data in real world recommender applications often feature fat-tailed distributions of the number of times individual items have been rated or favored. We propose a model to simulate such data. The model is mainly based on social interactions and opinion formation taking place on a complex network with a given topology. A threshold mechanism is used to govern the decision making process that determines whether a user is or is not interested in an item. We demonstrate the validity of the model by fitting attendance distributions from different real data sets. The model is mathematically analyzed by investigating its master equation. Our approach provides an attempt to understand recommender system's data as a social process. The model can serve as a starting point to generate artificial data sets useful for testing and evaluating recommender systems.

  1. Personalised Information Gathering and Recommender Systems: Techniques and Trends

    Directory of Open Access Journals (Sweden)

    Xiaohui Tao

    2013-02-01

    Full Text Available With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users.Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-the-art tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from a technological and social perspective. Aiming to promote high quality research in order to overcome these challenges, this paper provides a comprehensive survey on the recent work and achievements in the areas of personalised web information gathering and recommender systems. The report covers concept-based techniques exploited in personalised information gathering and recommender systems.

  2. Recommendation System of Program Based on REST Style

    Directory of Open Access Journals (Sweden)

    Song Jin Bao

    2016-01-01

    Full Text Available With the popularity of digital TV, TV programs have been on the increase no matter in both the number and species, which brought many choice to the users. Although the digital TV has increased largely in the selectivity, it has become a fussy process that users search for programs which they are interested in. So there is need to have an efficient program recommendation system to solve the problem that is “information overload” for users. It can not only help users to get the program which they require, but also bring convenience to people’s life. The program recommendation system named MyView is planned and designed, aimed to providing an efficient information platform. The system also involves intelligent recommendation. The information guide will trigger the recommendation engine after users registering information, the engine will accord to the data in the guide information to make the personalized program recommendation. The system was deployed in the Tomcat and Apache integration servers on my localhost, so it also belongs to the Web application based on J2EE platform. AJAX is used that can achieve a good user experience to develop web presentation layer on MyView PC browser with flexible interface performance. The background of business services uses the hierarchical form. MyView System uses the CXF framework and Hibernate to equip controller and data persistence layer in the Spring container. The overall framework of the system uses the REST style, in order to extend the performance and function later. Background service layer with uniform interface, marked by the URI resource. At the same time, HTTP requestion is submitted by the AJAX to obtain services provided by resources. Finally, we can analyze and summary the features of MyView System.

  3. Recommender systems for location-based social networks

    CERN Document Server

    Symeonidis, Panagiotis; Manolopoulos, Yannis

    2014-01-01

    Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of t...

  4. A Recommender System based on Idiotypic Artificial Immune Networks

    CERN Document Server

    Cayzer, Steve

    2008-01-01

    The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

  5. A Recommender System based on the Immune Network

    CERN Document Server

    Steve, Cayzer

    2008-01-01

    The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen - antibody interaction for matching and antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

  6. Recommended Practice for Patch Management of Control Systems

    Energy Technology Data Exchange (ETDEWEB)

    Steven Tom; Dale Christiansen; Dan Berrett

    2008-12-01

    A key component in protecting a nation’s critical infrastructure and key resources is the security of control systems. The term industrial control system refers to supervisory control and data acquisition, process control, distributed control, and any other systems that control, monitor, and manage the nation’s critical infrastructure. Critical Infrastructure and Key Resources (CIKR) consists of electric power generators, transmission systems, transportation systems, dam and water systems, communication systems, chemical and petroleum systems, and other critical systems that cannot tolerate sudden interruptions in service. Simply stated, a control system gathers information and then performs a function based on its established parameters and the information it receives. The patch management of industrial control systems software used in CIKR is inconsistent at best and nonexistent at worst. Patches are important to resolve security vulnerabilities and functional issues. This report recommends patch management practices for consideration and deployment by industrial control systems owners.

  7. Recommender Systems for Technology Enhanced Learning: Research Trends & Applications

    NARCIS (Netherlands)

    Manouselis, Nikos; Verbert, Katrien; Drachsler, Hendrik; Santos, Olga

    2014-01-01

    As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted in

  8. Product Recommendation System Based on Personal Preference Model Using CAM

    Science.gov (United States)

    Murakami, Tomoko; Yoshioka, Nobukazu; Orihara, Ryohei; Furukawa, Koichi

    Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively.

  9. Recommendations on ambulance cardiopulmonary resuscitation in basic life support systems.

    Science.gov (United States)

    Hock Ong, Marcus Eng; Shin, Sang Do; Sung, Soon Swee; Tanaka, Hideharu; Huei-Ming, Matthew; Song, Kyoung Jun; Nishiuchi, Tatsuya; Leong, Benjamin Sieu-Hon; Karim, Sarah Abdul; Lin, Chih-Hao; Ryoo, Hyun Wook; Ryu, Hyun Ho; Iwami, Taku; Kajino, Kentaro; Ko, Patrick Chow-In; Lee, Kyung Won; Sumetchotimaytha, Nathida; Swor, Robert; Myers, Brent; Mackey, Kevin; McNally, Bryan

    2013-01-01

    Cardiopulmonary resuscitation (CPR) during ambulance transport can be a safety risk for providers and can affect CPR quality. In many Asian countries with basic life support (BLS) systems, patients experiencing out-of-hospital cardiac arrest (OHCA) are routinely transported in ambulances in which CPR is performed. This paper aims to make recommendations on best practices for CPR during ambulance transport in BLS systems. A panel consisting of 20 experts (including 4 North Americans) in emergency medical services (EMS) and resuscitation science was selected, and met over two days. We performed a literature review and selected 33 candidate issues in five core areas. Using Delphi methodology, the issues were classified into dichotomous (yes/no), multiple choice, and ranking questions. Primary consensus between experts was reached when there was more than 70% agreement. Questions with 60-69% agreement were made more specific and were submitted for a second round of voting. The panel agreed upon 24 consensus statements with more than 70% agreement (2 rounds of voting). The recommendations cover the following: length of time on the scene; advanced airway at the scene; CPR prior to transport; rhythm analysis and defibrillation during transport; prehospital interventions; field termination of resuscitation (TOR); consent for TOR; destination hospital; transport protocol; number of staff members; restraint systems; mechanical CPR; turning off of the engine for rhythm analysis; alternative CPR; and feedback for CPR quality. Recommendations for CPR during ambulance transport were developed using the Delphi method. These recommendations should be validated in clinical settings.

  10. A Recommender System in the Cyber Defense Domain

    Science.gov (United States)

    2014-03-27

    Constraint-based recommender systems: technologies and research issues”. Proceedings of the 10th international conference on Electronic commerce , ICEC... Electronic Commerce ”. In Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, 78–83, 2000. [28] Walker-Brown, Andrew. “The art of the...systems have been studied for decades, but only in their original domain of retail customer suggestions. The same algorithms and techniques could have

  11. A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

    OpenAIRE

    Wanaskar, Ujwala; Vij, Sheetal; Mukhopadhyay, Debajyoti

    2013-01-01

    As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given us...

  12. Location-aware News Recommendation System with Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Mehdi Nejati

    2016-10-01

    Full Text Available with release of a huge amount of news on the Internet and the trend of users to Web-based news services.it is necessary to have a recommendation system. To grab attentions to news, news services use a number of criteria that called news values and user location is an important factor for it. In this paper, LONEF is proposed as a tow stage recommendation system. In first stage news are ranked by user’s locations and in second stage news are recommended by location Preferences, recency, Trustworthiness, groups priorities and popularity. To reduce ambiguity these properties is used tow Mamdani fuzzy interference and case-based decision systems. In Mamdani fuzzy interference system, it is tried to increase the system speed by optimizing selection of rules and membership functions and because of ambiguous feedback implementation, a decision making system is used to enable better simulation of user’s activities. Performance of our proposed approach is demonstrated in the experiments on different news groups.

  13. Update of EULAR recommendations for the treatment of systemic sclerosis.

    Science.gov (United States)

    Kowal-Bielecka, Otylia; Fransen, Jaap; Avouac, Jerome; Becker, Mike; Kulak, Agnieszka; Allanore, Yannick; Distler, Oliver; Clements, Philip; Cutolo, Maurizio; Czirjak, Laszlo; Damjanov, Nemanja; Del Galdo, Francesco; Denton, Christopher P; Distler, Jörg H W; Foeldvari, Ivan; Figelstone, Kim; Frerix, Marc; Furst, Daniel E; Guiducci, Serena; Hunzelmann, Nicolas; Khanna, Dinesh; Matucci-Cerinic, Marco; Herrick, Ariane L; van den Hoogen, Frank; van Laar, Jacob M; Riemekasten, Gabriela; Silver, Richard; Smith, Vanessa; Sulli, Alberto; Tarner, Ingo; Tyndall, Alan; Welling, Joep; Wigley, Frederic; Valentini, Gabriele; Walker, Ulrich A; Zulian, Francesco; Müller-Ladner, Ulf

    2017-08-01

    The aim was to update the 2009 European League against Rheumatism (EULAR) recommendations for the treatment of systemic sclerosis (SSc), with attention to new therapeutic questions. Update of the previous treatment recommendations was performed according to EULAR standard operating procedures. The task force consisted of 32 SSc clinical experts from Europe and the USA, 2 patients nominated by the pan-European patient association for SSc (Federation of European Scleroderma Associations (FESCA)), a clinical epidemiologist and 2 research fellows. All centres from the EULAR Scleroderma Trials and Research group were invited to submit and select clinical questions concerning SSc treatment using a Delphi approach. Accordingly, 46 clinical questions addressing 26 different interventions were selected for systematic literature review. The new recommendations were based on the available evidence and developed in a consensus meeting with clinical experts and patients. The procedure resulted in 16 recommendations being developed (instead of 14 in 2009) that address treatment of several SSc-related organ complications: Raynaud's phenomenon (RP), digital ulcers (DUs), pulmonary arterial hypertension (PAH), skin and lung disease, scleroderma renal crisis and gastrointestinal involvement. Compared with the 2009 recommendations, the 2016 recommendations include phosphodiesterase type 5 (PDE-5) inhibitors for the treatment of SSc-related RP and DUs, riociguat, new aspects for endothelin receptor antagonists, prostacyclin analogues and PDE-5 inhibitors for SSc-related PAH. New recommendations regarding the use of fluoxetine for SSc-related RP and haematopoietic stem cell transplantation for selected patients with rapidly progressive SSc were also added. In addition, several comments regarding other treatments addressed in clinical questions and suggestions for the SSc research agenda were formulated. These updated data-derived and consensus-derived recommendations will help

  14. Recommender Systems for the Conference Paper Assignment Problem

    CERN Document Server

    Conry, Don; Ramakrishnan, Naren

    2009-01-01

    Conference paper assignment, i.e., the task of assigning paper submissions to reviewers, presents multi-faceted issues for recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account aspects such as: reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of interest, and an overall distribution of assignments that balances reviewer preferences with conference objectives. Among these, issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of paper-reviewer assignment. In this paper, we present an integrated study of both these aspects. First, due to the paucity of data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn paper-reviewer preference models. Second, our models are evaluated not just in terms of prediction accu...

  15. Applications of CCSDS recommendations to Integrated Ground Data Systems (IGDS)

    Science.gov (United States)

    Mizuta, Hiroshi; Martin, Daniel; Kato, Hatsuhiko; Ihara, Hirokazu

    1993-01-01

    This paper describes an application of the CCSDS Principle Network (CPH) service model to communications network elements of a postulated Integrated Ground Data System (IGDS). Functions are drawn principally from COSMICS (Cosmic Information and Control System), an integrated space control infrastructure, and the Earth Observing System Data and Information System (EOSDIS) Core System (ECS). From functional requirements, this paper derives a set of five communications network partitions which, taken together, support proposed space control infrastructures and data distribution systems. Our functional analysis indicates that the five network partitions derived in this paper should effectively interconnect the users, centers, processors, and other architectural elements of an IGDS. This paper illustrates a useful application of the CCSDS (Consultive Committee for Space Data Systems) Recommendations to ground data system development.

  16. Music recommendation system for biofied building considering multiple residents

    Science.gov (United States)

    Ito, Takahiro; Mita, Akira

    2012-04-01

    This research presents a music recommendation system based on multiple users' communication excitement and productivity. Evaluation is conducted on following two points. 1, Does songA recommended by the system improve the situation of dropped down communication excitement? 2, Does songB recommended by the system improve the situation of dropped down and productivity of collaborative work? The objective of this system is to recommend songs which shall improve the situation of dropped down communication excitement and productivity. Songs are characterized according to three aspects; familiarity, relaxing and BPM(Beat Per Minutes). Communication excitement is calculated from speech data obtained by an audio sensor. Productivity of collaborative brainstorming is manually calculated by the number of time-series key words during mind mapping. First experiment was music impression experiment to 118 students. Based on 1, average points of familiarity, relaxing and BPM 2, cronbach alpha factor, songA(high familiarity, high relaxing and high BPM song) and songB(high familiarity, high relaxing and low BPM) are selected. Exploratory experiment defined dropped down communication excitement and dropped down and productivity of collaborative work. Final experiment was conducted to 32 first meeting students divided into 8 groups. First 4 groups had mind mapping 1 while listening to songA, then had mind mapping 2 while listening songB. Following 4 groups had mind mapping 1 while listening to songB, then had mind mapping 2 while listening songA. Fianl experiment shows two results. Firstly, ratio of communication excitement between music listening section and whole brain storming is 1.27. Secondly, this system increases 69% of average productivity.

  17. Coarse cluster enhancing collaborative recommendation for social network systems

    Science.gov (United States)

    Zhao, Yao-Dong; Cai, Shi-Min; Tang, Ming; Shang, Min-Sheng

    2017-10-01

    Traditional collaborative filtering based recommender systems for social network systems bring very high demands on time complexity due to computing similarities of all pairs of users via resource usages and annotation actions, which thus strongly suppresses recommending speed. In this paper, to overcome this drawback, we propose a novel approach, namely coarse cluster that partitions similar users and associated items at a high speed to enhance user-based collaborative filtering, and then develop a fast collaborative user model for the social tagging systems. The experimental results based on Delicious dataset show that the proposed model is able to dramatically reduce the processing time cost greater than 90 % and relatively improve the accuracy in comparison with the ordinary user-based collaborative filtering, and is robust for the initial parameter. Most importantly, the proposed model can be conveniently extended by introducing more users' information (e.g., profiles) and practically applied for the large-scale social network systems to enhance the recommending speed without accuracy loss.

  18. Visuo-perceptive priming in Alzheimer's disease: evidence for a multi-componential implicit memory system.

    Science.gov (United States)

    Boccia, Maddalena; Silveri, Maria Caterina; Guariglia, Cecilia

    2014-01-01

    In the past two decades research has highlighted how implicit memory processes are spared in degenerative disorders, such as Alzheimer's disease (AD), which are characterized by the early onset of explicit memory deficits. However, according to recent studies, there may be dissociations among different types of implicit memory. Although several studies have shown selective sparing of lexical priming in AD, it is not completely clear what happens to other types of implicit memory processes, such as visuo-perceptual priming. The present study examines the possibility that the visuo-perceptual priming effect is spared in AD. We tested two groups of participants, i.e., a group of AD patients and a group of healthy age-matched controls, using a visuo-perceptual priming task. The task required the identification of fragmented pictures. Results showed a deficient priming effect in AD patients when it was measured by an Identification of Fragmented Pictures task. We discuss our results in light of the current hypothesis of a functional segregation in priming processes.

  19. Enabling Open Research Data Discovery through a Recommender System

    Science.gov (United States)

    Devaraju, Anusuriya; Jayasinghe, Gaya; Klump, Jens; Hogan, Dominic

    2017-04-01

    Government agencies, universities, research and nonprofit organizations are increasingly publishing their datasets to promote transparency, induce new research and generate economic value through the development of new products or services. The datasets may be downloaded from various data portals (data repositories) which are general or domain-specific. The Registry of Research Data Repository (re3data.org) lists more than 2500 such data repositories from around the globe. Data portals allow keyword search and faceted navigation to facilitate discovery of research datasets. However, the volume and variety of datasets have made finding relevant datasets more difficult. Common dataset search mechanisms may be time consuming, may produce irrelevant results and are primarily suitable for users who are familiar with the general structure and contents of the respective database. Therefore, we need new approaches to support research data discovery. Recommender systems offer new possibilities for users to find datasets that are relevant to their research interests. This study presents a recommender system developed for the CSIRO Data Access Portal (DAP, http://data.csiro.au). The datasets hosted on the portal are diverse, published by researchers from 13 business units in the organisation. The goal of the study is not to replace the current search mechanisms on the data portal, but rather to extend the data discovery through an exploratory search, in this case by building a recommender system. We adopted a hybrid recommendation approach, comprising content-based filtering and item-item collaborative filtering. The content-based filtering computes similarities between datasets based on metadata such as title, keywords, descriptions, fields of research, location, contributors, etc. The collaborative filtering utilizes user search behaviour and download patterns derived from the server logs to determine similar datasets. Similarities above are then combined with different

  20. Tag and Neighbor based Recommender systems for Medical events

    DEFF Research Database (Denmark)

    Bayyapu, Karunakar Reddy; Dolog, Peter

    2010-01-01

    This paper presents an extension of a multifactor recommendation approach based on user tagging with term neighbours. Neighbours of words in tag vectors and documents provide for hitting larger set of documents and not only those matching with direct tag vectors or content of the documents. Tag...... in the situations where the quality of tags is lower. We discuss the approach on the examples from the existing Medworm system to indicate the usefulness of the approach....

  1. Tag and Neighbor based Recommender systems for Medical events

    DEFF Research Database (Denmark)

    Bayyapu, Karunakar Reddy; Dolog, Peter

    This paper presents an extension of a multifactor recommendation approach based on user tagging with term neighbours. Neighbours of words in tag vectors and documents provide for hitting larger set of documents and not only those matching with direct tag vectors or content of the documents. Tag...... in the situations where the quality of tags is lower. We discuss the approach on the examples from the existing Medworm system to indicate the usefulness of the approach....

  2. Analysis and Use of MapReduce for Recommender Systems

    OpenAIRE

    Vezočnik, Melanija

    2014-01-01

    MapReduce is a programming model for developing scalable parallel applications for processing large data sets, an execution framework that supports the programming model and coordinates the execution of programs and an implementation of the programming model and the execution framework. The goal of the thesis is to analyse MapReduce and to use it on two examples of recommender systems. The goal is achieved by developing the computation with MapReduce successfully. At first the programming mod...

  3. Multimedia services in intelligent environments advances in recommender systems

    CERN Document Server

    Virvou, Maria; Jain, Lakhmi

    2013-01-01

    Multimedia services are now commonly used in various activities in the daily lives of humans. Related application areas include services that allow access to large depositories of information, digital libraries, e-learning and e-education, e-government and e-governance, e-commerce and e-auctions, e-entertainment, e-health and e-medicine, and e-legal services, as well as their mobile counterparts (i.e., m-services). Despite the tremendous growth of multimedia services over the recent years, there is an increasing demand for their further development. This demand is driven by the ever-increasing desire of society for easy accessibility to information in friendly, personalized and adaptive environments. In this book at hand, we examine recent Advances in Recommender Systems. Recommender systems are crucial in multimedia services, as they aim at protecting the service users from information overload. The book includes nine chapters, which present various recent research results in recommender systems. This resear...

  4. Dynamic Grover search: applications in recommendation systems and optimization problems

    Science.gov (United States)

    Chakrabarty, Indranil; Khan, Shahzor; Singh, Vanshdeep

    2017-06-01

    In the recent years, we have seen that Grover search algorithm (Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996) by using quantum parallelism has revolutionized the field of solving huge class of NP problems in comparisons to classical systems. In this work, we explore the idea of extending Grover search algorithm to approximate algorithms. Here we try to analyze the applicability of Grover search to process an unstructured database with a dynamic selection function in contrast to the static selection function used in the original work (Grover in Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996). We show that this alteration facilitates us to extend the application of Grover search to the field of randomized search algorithms. Further, we use the dynamic Grover search algorithm to define the goals for a recommendation system based on which we propose a recommendation algorithm which uses binomial similarity distribution space giving us a quadratic speedup over traditional classical unstructured recommendation systems. Finally, we see how dynamic Grover search can be used to tackle a wide range of optimization problems where we improve complexity over existing optimization algorithms.

  5. Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

    Directory of Open Access Journals (Sweden)

    Zhong-Da Tian

    2015-01-01

    Full Text Available The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC. The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support vector machine time-delay prediction model are difficult to determine, and the genetic algorithm is used for least squares support vector machine optimal prediction parameter optimization. Then, an improved implicit generalized predictive control method is adopted to compensate for the time delay. The simulation results show that the method in this paper has high prediction accuracy and a good compensation effect for the random time delay of the networked control system, has a small amount of on-line calculation and that the output response and control stability of the system are improved.

  6. Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System

    Directory of Open Access Journals (Sweden)

    A. A. Salama

    2015-03-01

    Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.

  7. I should not recommend it to you even if you will like it: the ethics of recommender systems

    Science.gov (United States)

    Tang, Tiffany Ya; Winoto, Pinata

    2016-01-01

    In this paper, we extend the current research in the recommendation system community by showing that users did attach ethical consideration to items. In an experiment (N = 111) that manipulated several moral factors regarding the potentially harmful content in movies, books, and games, users were asked to evaluate the appropriateness of recommending these items to teenagers and adult couples. Results agreed with previous studies in that gender plays a key role in making moral judgment, especially regarding the ethical appropriateness of an item. The pilot study further identifies degrees of aversion regarding the appeal of these elements in media for ethical recommendations. Based on the study, we propose a user-initiated ethical recommender system to help users pick up morally appropriate items during the post-recommendation process. We believe that the ethical appropriateness of items perceived by end users could predict the trust and credibility of the system.

  8. Combination of evidence in recommendation systems characterized by distance functions

    Energy Technology Data Exchange (ETDEWEB)

    Rocha, L. M. (Luis Mateus)

    2002-01-01

    Recommendation systems for different Document Networks (DN) such as the World Wide Web (WWW), Digitnl Libarries, or Scientific Databases, often make use of distance functions extracted from relationships among documents and between documents and semantic tags. For instance, documents In the WWW are related via a hyperlink network, while documents in bibliographic databases are related by citation and collaboration networks.Furthermore, documents can be related to semantic tags such as keywords used to describe their content, The distance functions computed from these relations establish associative networks among items of the DN, and allow recommendation systems to identify relevant associations for iudividoal users. The process of recommendation can be improved by integrating associative data from different sources. Thus we are presented with a problem of combining evidence (about assochaons between items) from different sonrces characterized by distance functions. In this paper we summarize our work on (1) inferring associations from semi-metric distance functions and (2) combining evidence from different (distance) associative DN.

  9. Recommendations of the workshop on advanced geothermal drilling systems

    Energy Technology Data Exchange (ETDEWEB)

    Glowka, D.A.

    1997-12-01

    At the request of the U.S. Department of Energy, Office of Geothermal Technologies, Sandia National Laboratories convened a group of drilling experts in Berkeley, CA, on April 15-16, 1997, to discuss advanced geothermal drilling systems. The objective of the workshop was to develop one or more conceptual designs for an advanced geothermal drilling system that meets all of the criteria necessary to drill a model geothermal well. The drilling process was divided into ten essential functions. Each function was examined, and discussions were held on the conventional methods used to accomplish each function and the problems commonly encountered. Alternative methods of performing each function were then listed and evaluated by the group. Alternative methods considered feasible or at least worth further investigation were identified, while methods considered impractical or not potentially cost-saving were eliminated from further discussion. This report summarizes the recommendations of the workshop participants. For each of the ten functions, the conventional methods, common problems, and recommended alternative technologies and methods are listed. Each recommended alternative is discussed, and a description is given of the process by which this information will be used by the U.S. DOE to develop an advanced geothermal drilling research program.

  10. Modeling Temporal Bias of Uplift Events in Recommender Systems

    KAUST Repository

    Altaf, Basmah

    2013-05-08

    Today, commercial industry spends huge amount of resources in advertisement campaigns, new marketing strategies, and promotional deals to introduce their product to public and attract a large number of customers. These massive investments by a company are worthwhile because marketing tactics greatly influence the consumer behavior. Alternatively, these advertising campaigns have a discernible impact on recommendation systems which tend to promote popular items by ranking them at the top, resulting in biased and unfair decision making and loss of customers’ trust. The biasing impact of popularity of items on recommendations, however, is not fixed, and varies with time. Therefore, it is important to build a bias-aware recommendation system that can rank or predict items based on their true merit at given time frame. This thesis proposes a framework that can model the temporal bias of individual items defined by their characteristic contents, and provides a simple process for bias correction. Bias correction is done either by cleaning the bias from historical training data that is used for building predictive model, or by ignoring the estimated bias from the predictions of a standard predictor. Evaluated on two real world datasets, NetFlix and MovieLens, our framework is shown to be able to estimate and remove the bias as a result of adopted marketing techniques from the predicted popularity of items at a given time.

  11. Recommendations of the workshop on advanced geothermal drilling systems

    Energy Technology Data Exchange (ETDEWEB)

    Glowka, D.A.

    1997-12-01

    At the request of the U.S. Department of Energy, Office of Geothermal Technologies, Sandia National Laboratories convened a group of drilling experts in Berkeley, CA, on April 15-16, 1997, to discuss advanced geothermal drilling systems. The objective of the workshop was to develop one or more conceptual designs for an advanced geothermal drilling system that meets all of the criteria necessary to drill a model geothermal well. The drilling process was divided into ten essential functions. Each function was examined, and discussions were held on the conventional methods used to accomplish each function and the problems commonly encountered. Alternative methods of performing each function were then listed and evaluated by the group. Alternative methods considered feasible or at least worth further investigation were identified, while methods considered impractical or not potentially cost-saving were eliminated from further discussion. This report summarizes the recommendations of the workshop participants. For each of the ten functions, the conventional methods, common problems, and recommended alternative technologies and methods are listed. Each recommended alternative is discussed, and a description is given of the process by which this information will be used by the U.S. DOE to develop an advanced geothermal drilling research program.

  12. Persuasion and Recommendation System Applied to a Cognitive Assistant

    Directory of Open Access Journals (Sweden)

    Angelo COSTA

    2016-11-01

    Full Text Available In this paper, we present a persuasive recommendation module included in the iGenda framework. iGenda is a cognitive assistant that helps care-receivers and caregivers in the management of their activities of daily living, by resolving scheduling conflicts and promoting active aging activities. The proposed new module will allow the system to select and recommend to the users an event that potentially best suits to his/her interests (likes or medical condition. The multi-agent approach followed by the iGenda framework facilitates an easy integration of these new features. The social objective is to promote social activities and engaging the users in physical or psychological activities that improve their medical condition.

  13. Social Recommender Systems Based on Coupling Network Structure Analysis

    CERN Document Server

    Hu, Xiao; Chen, Xiaolong; Zhang, Zi-Ke

    2012-01-01

    The past few years has witnessed the great success of recommender systems, which can significantly help users find relevant and interesting items for them in the information era. However, a vast class of researches in this area mainly focus on predicting missing links in bipartite user-item networks (represented as behavioral networks). Comparatively, the social impact, especially the network structure based properties, is relatively lack of study. In this paper, we firstly obtain five corresponding network-based features, including user activity, average neighbors' degree, clustering coefficient, assortative coefficient and discrimination, from social and behavioral networks, respectively. A hybrid algorithm is proposed to integrate those features from two respective networks. Subsequently, we employ a machine learning process to use those features to provide recommendation results in a binary classifier method. Experimental results on a real dataset, Flixster, suggest that the proposed method can significan...

  14. Local Discontinuous Galerkin Methods Coupled with Implicit Integration Factor Methods for Solving Reaction-Cross-Diffusion Systems

    Directory of Open Access Journals (Sweden)

    Na An

    2016-01-01

    Full Text Available We present a new numerical method for solving nonlinear reaction-diffusion systems with cross-diffusion which are often taken as mathematical models for many applications in the biological, physical, and chemical sciences. The two-dimensional system is discretized by the local discontinuous Galerkin (LDG method on unstructured triangular meshes associated with the piecewise linear finite element spaces, which can derive not only numerical solutions but also approximations for fluxes at the same time comparing with most of study work up to now which has derived numerical solutions only. Considering the stability requirement for the explicit scheme with strict time step restriction (Δt=O(hmin2, the implicit integration factor (IIF method is employed for the temporal discretization so that the time step can be relaxed as Δt=O(hmin. Moreover, the method allows us to compute element by element and avoids solving a global system of nonlinear algebraic equations as the standard implicit schemes do, which can reduce the computational cost greatly. Numerical simulations about the system with exact solution and the Brusselator model, which is a theoretical model for a type of autocatalytic chemical reaction, are conducted to confirm the expected accuracy, efficiency, and advantages of the proposed schemes.

  15. Recommending Tags for New Resources in Social Bookmarking Systems

    Directory of Open Access Journals (Sweden)

    Shweta Yagnik

    2014-04-01

    Full Text Available Social bookmarking system is a web-based resource sharing system that allows users to upload, share and organize their resources i.e. bookmarks and publications. The system has shifted the paradigm of bookmarking from an individual activity limited to desktop to a collective activity on the web. It also facilitates user to annotate his resource with free form tags that leads to large communities of users to collaboratively create accessible repositories of web resources. Tagging process has its own challenges like ambiguity, redundancy or misspelled tags and sometimes user tends to avoid it as he has to describe tag at his own. The resultant tag space is noisy or very sparse and dilutes the purpose of tagging. The effective solution is Tag Recommendation System that automatically suggests appropriate set of tags to user while annotating resource. In this paper, we propose a framework that does not depend on tagging history of the resource or user and thereby capable of suggesting tags to the resources which are being submitted to the system first time. We model tag recommendation task as multi-label text classification problem and use Naive Bayes classifier as the baselearner of the multilabel classifier. We experiment with Boolean, bag-of-words and term frequency-inverse document frequency (TFIDF representation of the resources and fit appropriate distribution to the data based on the representation used. Impact of featureselection on the effectiveness of the tag recommendation is also studied. Effectiveness of the proposed framework is evaluated through precision, recall and f-measure metrics.

  16. COLLABORATIVE WEB RECOMMENDATION SYSTEMS BASED ON AN EFFECTIVE FUZZY ASSOCIATION RULE MINING ALGORITHM (FARM)

    OpenAIRE

    Dr. P. THAMBIDURAI; A.KUMAR,

    2010-01-01

    With increasing popularity of the web-based systems that are applied in many different areas, they tend to deliver customized informationfor their users by means of utilization of recommendation methods. This recommendation system is mainly classified into two groups:Content-based recommendation and collaborative recommendation system. Content based recommendation tries to recommend web sites similar to those web sites the user has liked, whereas collaborative ecommendation tries to find som...

  17. Improving electronic customers' profile in recommender systems using data mining techniques

    Directory of Open Access Journals (Sweden)

    Mohammad Julashokri

    2011-10-01

    Full Text Available Recommender systems are tools for realization one to one marketing. Recommender systems are systems, which attract, retain, and develop customers. Recommender systems use several ways to make recommendations. Two ways are using more than the others: collaborative filtering and content-based filtering. In this study, a recommender system model based on collaborative filtering has proposed. Proposed model was endeavored to improve the customer profile in collaborative systems to enhance the recommender system efficiency. This improvement was done using time context and group preferences. Experimental results show that the proposed model has a better recommendation performance than existing models.

  18. A manual of recommended practices for hydrogen energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Hoagland, W.; Leach, S. [W. Hoagland and Associates, Boulder, CO (United States)

    1997-12-31

    Technologies for the production, distribution, and use of hydrogen are rapidly maturing and the number and size of demonstration programs designed to showcase emerging hydrogen energy systems is expanding. The success of these programs is key to hydrogen commercialization. Currently there is no comprehensive set of widely-accepted codes or standards covering the installation and operation of hydrogen energy systems. This lack of codes or standards is a major obstacle to future hydrogen demonstrations in obtaining the requisite licenses, permits, insurance, and public acceptance. In a project begun in late 1996 to address this problem, W. Hoagland and Associates has been developing a Manual of Recommended Practices for Hydrogen Systems intended to serve as an interim document for the design and operation of hydrogen demonstration projects. It will also serve as a starting point for some of the needed standard-setting processes. The Manual will include design guidelines for hydrogen procedures, case studies of experience at existing hydrogen demonstration projects, a bibliography of information sources, and a compilation of suppliers of hydrogen equipment and hardware. Following extensive professional review, final publication will occur later in 1997. The primary goal is to develop a draft document in the shortest possible time frame. To accomplish this, the input and guidance of technology developers, industrial organizations, government R and D and regulatory organizations and others will be sought to define the organization and content of the draft Manual, gather and evaluate available information, develop a draft document, coordinate reviews and revisions, and develop recommendations for publication, distribution, and update of the final document. The workshop, Development of a Manual of Recommended Practices for Hydrogen Energy Systems, conducted on March 11, 1997 in Alexandria, Virginia, was a first step.

  19. The system neurophysiological basis of non-adaptive cognitive control: Inhibition of implicit learning mediated by right prefrontal regions.

    Science.gov (United States)

    Stock, Ann-Kathrin; Steenbergen, Laura; Colzato, Lorenza; Beste, Christian

    2016-12-01

    Cognitive control is adaptive in the sense that it inhibits automatic processes to optimize goal-directed behavior, but high levels of control may also have detrimental effects in case they suppress beneficial automatisms. Until now, the system neurophysiological mechanisms and functional neuroanatomy underlying these adverse effects of cognitive control have remained elusive. This question was examined by analyzing the automatic exploitation of a beneficial implicit predictive feature under conditions of high versus low cognitive control demands, combining event-related potentials (ERPs) and source localization. It was found that cognitive control prohibits the beneficial automatic exploitation of additional implicit information when task demands are high. Bottom-up perceptual and attentional selection processes (P1 and N1 ERPs) are not modulated by this, but the automatic exploitation of beneficial predictive information in case of low cognitive control demands was associated with larger response-locked P3 amplitudes and stronger activation of the right inferior frontal gyrus (rIFG, BA47). This suggests that the rIFG plays a key role in the detection of relevant task cues, the exploitation of alternative task sets, and the automatic (bottom-up) implementation and reprogramming of action plans. Moreover, N450 amplitudes were larger under high cognitive control demands, which was associated with activity differences in the right medial frontal gyrus (BA9). This most likely reflects a stronger exploitation of explicit task sets which hinders the exploration of the implicit beneficial information in case of high cognitive control demands. Hum Brain Mapp 37:4511-4522, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  20. A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

    NARCIS (Netherlands)

    Dietze, Stefan; Drachsler, Hendrik; Daniela, Giordano

    2014-01-01

    Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements.

  1. Educational Resources Recommendation System for a heterogeneous Student Group

    Directory of Open Access Journals (Sweden)

    Paula Andrea RODRÍGUEZ MARÍN

    2016-12-01

    Full Text Available In a face-class, where the student group is heterogeneous, it is necessary to select the most appropriate educational resources that support learning for all. In this sense, multi-agent system (MAS can be used to simulate the features of the students in the group, including their learning style, in order to help the professor find the best resources for your class. In this paper, we present MAS to educational resources recommendation for group students, simulating their profiles and selecting resources that best fit. Obtained promising results show that proposed MAS is able to delivered educational resources for a student group.

  2. Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems

    Institute of Scientific and Technical Information of China (English)

    YAO Yu; ZHU Shanfeng; CHEN Xinmeng

    2006-01-01

    In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users' ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.

  3. A Probability-Based Hybrid User Model for Recommendation System

    Directory of Open Access Journals (Sweden)

    Jia Hao

    2016-01-01

    Full Text Available With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

  4. Production Functions for Water Delivery Systems: Analysis and Estimation Using Dual Cost Function and Implicit Price Specifications

    Science.gov (United States)

    Teeples, Ronald; Glyer, David

    1987-05-01

    Both policy and technical analysis of water delivery systems have been based on cost functions that are inconsistent with or are incomplete representations of the neoclassical production functions of economics. We present a full-featured production function model of water delivery which can be estimated from a multiproduct, dual cost function. The model features implicit prices for own-water inputs and is implemented as a jointly estimated system of input share equations and a translog cost function. Likelihood ratio tests are performed showing that a minimally constrained, full-featured production function is a necessary specification of the water delivery operations in our sample. This, plus the model's highly efficient and economically correct parameter estimates, confirms the usefulness of a production function approach to modeling the economic activities of water delivery systems.

  5. Towards an Explanation Generation System for Robots: Analysis and Recommendations

    Directory of Open Access Journals (Sweden)

    Ben Meadows

    2016-10-01

    Full Text Available A fundamental challenge in robotics is to reason with incomplete domain knowledge to explain unexpected observations and partial descriptions extracted from sensor observations. Existing explanation generation systems draw on ideas that can be mapped to a multidimensional space of system characteristics, defined by distinctions, such as how they represent knowledge and if and how they reason with heuristic guidance. Instances in this multidimensional space corresponding to existing systems do not support all of the desired explanation generation capabilities for robots. We seek to address this limitation by thoroughly understanding the range of explanation generation capabilities and the interplay between the distinctions that characterize them. Towards this objective, this paper first specifies three fundamental distinctions that can be used to characterize many existing explanation generation systems. We explore and understand the effects of these distinctions by comparing the capabilities of two systems that differ substantially along these axes, using execution scenarios involving a robot waiter assisting in seating people and delivering orders in a restaurant. The second part of the paper uses this study to argue that the desired explanation generation capabilities corresponding to these three distinctions can mostly be achieved by exploiting the complementary strengths of the two systems that were explored. This is followed by a discussion of the capabilities related to other major distinctions to provide detailed recommendations for developing an explanation generation system for robots.

  6. Method and system to discover and recommend interesting documents

    Energy Technology Data Exchange (ETDEWEB)

    Potok, Thomas Eugene; Steed, Chad Allen; Patton, Robert Matthew

    2017-01-31

    Disclosed are several examples of systems that can read millions of news feeds per day about topics (e.g., your customers, competitors, markets, and partners), and provide a small set of the most relevant items to read to keep current with the overwhelming amount of information currently available. Topics of interest can be chosen by the user of the system for use as seeds. The seeds can be vectorized and compared with the target documents to determine their similarity. The similarities can be sorted from highest to lowest so that the most similar seed and target documents are at the top of the list. This output can be produced in XML format so that an RSS Reader can format the XML. This allows for easy Internet access to these recommendations.

  7. Explicit and implicit ode solvers using Krylov subspace optimization: Application to the diffusion equation and parabolic Maxwell`s system

    Energy Technology Data Exchange (ETDEWEB)

    Druskin, V.; Knizhnerman, L.

    1994-12-31

    The authors solve the Cauchy problem for an ODE system Au + {partial_derivative}u/{partial_derivative}t = 0, u{vert_bar}{sub t=0} = {var_phi}, where A is a square real nonnegative definite symmetric matrix of the order N, {var_phi} is a vector from R{sup N}. The stiffness matrix A is obtained due to semi-discretization of a parabolic equation or system with time-independent coefficients. The authors are particularly interested in large stiff 3-D problems for the scalar diffusion and vectorial Maxwell`s equations. First they consider an explicit method in which the solution on a whole time interval is projected on a Krylov subspace originated by A. Then they suggest another Krylov subspace with better approximating properties using powers of an implicit transition operator. These Krylov subspace methods generate optimal in a spectral sense polynomial approximations for the solution of the ODE, similar to CG for SLE.

  8. A comparison of clustering algorithms in article recommendation system

    Science.gov (United States)

    Tantanasiriwong, Supaporn

    2012-01-01

    Recommendation system is considered a tool that can be used to recommend researchers about resources that are suitable for their research of interest by using content-based filtering. In this paper, clustering algorithm as an unsupervised learning is introduced for grouping objects based on their feature selection and similarities. The information of publication in Science Cited Index is used to be dataset for clustering as a feature extraction in terms of dimensionality reduction of these articles by comparing Latent Dirichlet Allocation (LDA), Principal Component Analysis (PCA), and K-Mean to determine the best algorithm. In my experiment, the selected database consists of 2625 documents extraction extracted from SCI corpus from 2001 to 2009. Clustering into ranks as 50,100,200,250 is used to consider and using F-Measure evaluate among them in three algorithms. The result of this paper showed that LDA technique given the accuracy up to 95.5% which is the highest effective than any other clustering technique.

  9. A Proactive Recommendation System for Writing in the Internet Age

    Directory of Open Access Journals (Sweden)

    Olga Muñoz

    2010-03-01

    Full Text Available With the use of the computers, the task of writing is intertwined with the task of searching for information that can be relevant for the document that is being written, however very little research has been done to understand how the two tasks intertwine. In this paper we present an initial attempt to develop a model of writing and information seeking with computers and to develop helpful software that can improve the quality of the information searched and the written paper. Proactive Recommendation System (PRS can relieve authors from explicit searching by means of automatically searching, retrieving and recommending information relevant to the text currently being written, and therefore PRS can be helpful to writers. However it is also possible that there are some moments during writing in which presenting proactive information can be an interruption rather than a help. In our research, we have used the PRS IntelliGent™ to investigate its impact in the different stages of writing. We found that when IntelliGent™ offers relevant information the time to task completion is shorter and the quality of the written product increases compared with the control situations in which writers have to look actively for information. We discuss these findings in the context of developing models and tools that integrate searching and writing processes when using computers as the writing environment.

  10. Near2me: An authentic and personalized social media-based recommender for travel destinations

    DEFF Research Database (Denmark)

    Kofler, Christoph; Caballero, Luz; Menendez Blanco, Maria

    2011-01-01

    This paper presents Near2me, a prototype system implementing a travel recommender concept that generates recommendations that are not only personalized, but also authentic. Exploitation of implicit situational knowledge makes it possible for Near2me to recommend places that are not necessarily to...

  11. Method and system of filtering and recommending documents

    Energy Technology Data Exchange (ETDEWEB)

    Patton, Robert M.; Potok, Thomas E.

    2016-02-09

    Disclosed is a method and system for discovering documents using a computer and providing a small set of the most relevant documents to the attention of a human observer. Using the method, the computer obtains a seed document from the user and generates a seed document vector using term frequency-inverse corpus frequency weighting. A keyword index for a plurality of source documents can be compared with the weighted terms of the seed document vector. The comparison is then filtered to reduce the number of documents, which define an initial subset of the source documents. Initial subset vectors are generated and compared to the seed document vector to obtain a similarity value for each comparison. Based on the similarity value, the method then recommends one or more of the source documents.

  12. Analysis on Recommended System for Web Information Retrieval Using HMM

    Directory of Open Access Journals (Sweden)

    Himangni Rathore

    2014-11-01

    Full Text Available Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The number of users is continuously access the information over the internet. Web mining is an application of data mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used in the domain of web information mining is refers as web mining, that is further divided into three major domains web uses mining, web content mining and web structure mining. The proposed work is intended to work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data, which method promises to provide much accurate recommendation.

  13. Mathematical Modeling of Competitive Group Recommendation Systems with Application to Peer Review Systems

    CERN Document Server

    Xie, Hong

    2012-01-01

    In this paper, we present a mathematical model to capture various factors which may influence the accuracy of a competitive group recommendation system. We apply this model to peer review systems, i.e., conference or research grants review, which is an essential component in our scientific community. We explore number of important questions, i.e., how will the number of reviews per paper affect the accuracy of the overall recommendation? Will the score aggregation policy influence the final recommendation? How reviewers' preference may affect the accuracy of the final recommendation? To answer these important questions, we formally analyze our model. Through this analysis, we obtain the insight on how to design a randomized algorithm which is both computationally efficient and asymptotically accurate in evaluating the accuracy of a competitive group recommendation system. We obtain number of interesting observations: i.e., for a medium tier conference, three reviews per paper is sufficient for a high accuracy...

  14. An international survey and recommendations for modern hydrokinetic systems

    Science.gov (United States)

    Valyrakis, Manousos; Basnet, Bipin; Dunsmore, Ian

    2017-04-01

    This study presents the results of a survey on some of the advantages of the novel and uniquehydrokinetic energy generation systems over other technologies available today. Recently, a comprehensive assessment study for the application of internationally leading hydrokinetic technologies in water engineering has been conducted. The study was carried with the collaboration of the School of Engineering, University of Glasgow and Scottish Water Horizons Ltd. The assessment involved the information collection, critical analysis of various features and financial viability analysis of various hydrokinetic systems available at this time. The outcomes of the study are summarized below: - The preliminary assessment of the hydrokinetic system and their application were carried out. The technologies were divided into different categories as per their core theory, scope of application as well as positive and negatives effects of their application. - A variety of criteria were used to assess the technical, economical and ecological potential from the application of hydrokinetic systems. - A number of companies representing a wide range of technologies available worldwide were ranked considering the performance of these against the above criteria. - Only a couple of the companies could satisfy the selection condition to be adopted into select sites of low flow and low pressure head. - A more detailed assessment for specific sites and further testing of these technologies is recommended to further assess the advantages and optimal performance of the selected technologies. A preliminary evaluation of the best performing systems demonstrates its effectiveness, particularly over other existing hydrokinetic technologies, when ecology of the open water surface system is considered. Specifically it will be of interest to use the selected technology in combination with a fish passage, as compared with other technologies this system has a minimal amount of fast moving components.

  15. Hybrid Trust-Driven Recommendation System for E-commerce Networks

    Directory of Open Access Journals (Sweden)

    Pavan Kumar K. N

    Full Text Available In traditional recommendation systems, the challenging issues in adopting similarity-based approaches are sparsity, cold-start users and trustworthiness. We present a new paradigm of recommendation system which can utilize information from social networks ...

  16. Consumers’ intention to use health recommendation systems to receive personalized nutrition advice

    NARCIS (Netherlands)

    S. Wendel (Sonja); B.G.C. Dellaert (Benedict); A. Ronteltap (Amber); H.C.M. van Trijp (Hans)

    2013-01-01

    markdownabstract__Abstract__ __Background:__ Sophisticated recommendation systems are used more and more in the health sector to assist consumers in healthy decision making. In this study we investigate consumers' evaluation of hypothetical health recommendation systems that provide personalized n

  17. Consumers’ intention to use health recommendation systems to receive personalized nutrition advice

    NARCIS (Netherlands)

    Wendel, S.; Dellaert, B.G.C.; Ronteltap, A.; Trijp, van J.C.M.

    2013-01-01

    Background: Sophisticated recommendation systems are used more and more in the health sector to assist consumers in healthy decision making. In this study we investigate consumers' evaluation of hypothetical health recommendation systems that provide personalized nutrition advice. We examine consume

  18. Consumers’ intention to use health recommendation systems to receive personalized nutrition advice

    NARCIS (Netherlands)

    Wendel, S.; Dellaert, B.G.C.; Ronteltap, A.; Trijp, van J.C.M.

    2013-01-01

    Background: Sophisticated recommendation systems are used more and more in the health sector to assist consumers in healthy decision making. In this study we investigate consumers' evaluation of hypothetical health recommendation systems that provide personalized nutrition advice. We examine consume

  19. Implicit Numerical Methods in Meteorology

    Science.gov (United States)

    Augenbaum, J.

    1984-01-01

    The development of a fully implicit finite-difference model, whose time step is chosen solely to resolve accurately the physical flow of interest is discussed. The method is based on an operator factorization which reduces the dimensionality of the implicit approach: at each time step only (spatially) one-dimensional block-tridiagonal linear systems must be solved. The scheme uses two time levels and is second-order accurate in time. Compact implicit spatial differences are used, yielding fourth-order accuracy both vertically and horizontally. In addition, the development of a fully interactive computer code is discussed. With this code the user will have a choice of models, with various levels of accuracy and sophistication, which are imbedded, as subsets of the fully implicit 3D code.

  20. A Weight-Aware Recommendation Algorithm for Mobile Multimedia Systems

    Directory of Open Access Journals (Sweden)

    Pedro M. P. Rosa

    2013-01-01

    Full Text Available In the last years, information flood is becoming a common reality, and the general user, hit by thousands of possible interesting information, has great difficulties identifying the best ones, that can guide him in his/her daily choices, like concerts, restaurants, sport gatherings, or culture events. The current growth of mobile smartphones and tablets with embedded GPS receiver, Internet access, camera, and accelerometer offer new opportunities to mobile ubiquitous multimedia applications that helps gathering the best information out of an always growing list of possibly good ones. This paper presents a mobile recommendation system for events, based on few weighted context-awareness data-fusion algorithms to combine several multimedia sources. A demonstrative deployment were utilized relevance like location data, user habits and user sharing statistics, and data-fusion algorithms like the classical CombSUM and CombMNZ, simple, and weighted. Still, the developed methodology is generic, and can be extended to other relevance, both direct (background noise volume and indirect (local temperature extrapolated by GPS coordinates in a Web service and other data-fusion techniques. To experiment, demonstrate, and evaluate the performance of different algorithms, the proposed system was created and deployed into a working mobile application providing real time awareness-based information of local events and news.

  1. How to use recommender systems in e-business domains

    Directory of Open Access Journals (Sweden)

    Umberto Panniello

    2014-12-01

    Full Text Available Recommender systems (RS were developed by research as a means to manage the information retrieval problem for users searching large databases. Recently they have become very popular among businesses as online marketing tools. Several online companies base their success on these systems, among other conditions. By looking at the last decades, the research on RS can be summarized into two main streams. The first research stream is focused on technical aspects of the algorithms and on identifying new ways to make them more accurate, while the second stream is focused on the effects of RS on customers. Therefore, we can draw several indications from the research on RS about the mistakes that companies should avoid when using RS. In this work we conduct an extensive literature and industrial review and we identify some crucial points managers should mind when developing a RS in order to make it as effective as possible in real world applications, or at least to avoid making it a failure.

  2. Discretization and implicit mapping dynamics

    CERN Document Server

    Luo, Albert C J

    2015-01-01

    This unique book presents the discretization of continuous systems and implicit mapping dynamics of periodic motions to chaos in continuous nonlinear systems. The stability and bifurcation theory of fixed points in discrete nonlinear dynamical systems is reviewed, and the explicit and implicit maps of continuous dynamical systems are developed through the single-step and multi-step discretizations. The implicit dynamics of period-m solutions in discrete nonlinear systems are discussed. The book also offers a generalized approach to finding analytical and numerical solutions of stable and unstable periodic flows to chaos in nonlinear systems with/without time-delay. The bifurcation trees of periodic motions to chaos in the Duffing oscillator are shown as a sample problem, while the discrete Fourier series of periodic motions and chaos are also presented. The book offers a valuable resource for university students, professors, researchers and engineers in the fields of applied mathematics, physics, mechanics,...

  3. Convergence Analysis of Semi-Implicit Euler Methods for Solving Stochastic Age-Dependent Capital System with Variable Delays and Random Jump Magnitudes

    Directory of Open Access Journals (Sweden)

    Qinghui Du

    2014-01-01

    Full Text Available We consider semi-implicit Euler methods for stochastic age-dependent capital system with variable delays and random jump magnitudes, and investigate the convergence of the numerical approximation. It is proved that the numerical approximate solutions converge to the analytical solutions in the mean-square sense under given conditions.

  4. Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

    Science.gov (United States)

    Mihai, Gabroveanu

    2015-09-01

    Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

  5. Recommended Practice: Creating Cyber Forensics Plans for Control Systems

    Energy Technology Data Exchange (ETDEWEB)

    Eric Cornelius; Mark Fabro

    2008-08-01

    issues and to accommodate for the diversity in both system and architecture types, a framework based in recommended practices to address forensics in the control systems domain is required. This framework must be fully flexible to allow for deployment into any control systems environment regardless of technologies used. Moreover, the framework and practices must provide for direction on the integration of modern network security technologies with traditionally closed systems, the result being a true defense-in-depth strategy for control systems architectures. This document takes the traditional concepts of cyber forensics and forensics engineering and provides direction regarding augmentation for control systems operational environments. The goal is to provide guidance to the reader with specifics relating to the complexity of cyber forensics for control systems, guidance to allow organizations to create a self-sustaining cyber forensics program, and guidance to support the maintenance and evolution of such programs. As the current control systems cyber security community of interest is without any specific direction on how to proceed with forensics in control systems environments, this information product is intended to be a first step.

  6. A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users.

    Science.gov (United States)

    Ravi, Logesh; Vairavasundaram, Subramaniyaswamy

    2016-01-01

    Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.

  7. A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

    Directory of Open Access Journals (Sweden)

    Logesh Ravi

    2016-01-01

    Full Text Available Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.

  8. Measuring implicit attitudes: A positive framing bias flaw in the Implicit Relational Assessment Procedure (IRAP).

    Science.gov (United States)

    O'Shea, Brian; Watson, Derrick G; Brown, Gordon D A

    2016-02-01

    How can implicit attitudes best be measured? The Implicit Relational Assessment Procedure (IRAP), unlike the Implicit Association Test (IAT), claims to measure absolute, not just relative, implicit attitudes. In the IRAP, participants make congruent (Fat Person-Active: false; Fat Person-Unhealthy: true) or incongruent (Fat Person-Active: true; Fat Person-Unhealthy: false) responses in different blocks of trials. IRAP experiments have reported positive or neutral implicit attitudes (e.g., neutral attitudes toward fat people) in cases in which negative attitudes are normally found on explicit or other implicit measures. It was hypothesized that these results might reflect a positive framing bias (PFB) that occurs when participants complete the IRAP. Implicit attitudes toward categories with varying prior associations (nonwords, social systems, flowers and insects, thin and fat people) were measured. Three conditions (standard, positive framing, and negative framing) were used to measure whether framing influenced estimates of implicit attitudes. It was found that IRAP scores were influenced by how the task was framed to the participants, that the framing effect was modulated by the strength of prior stimulus associations, and that a default PFB led to an overestimation of positive implicit attitudes when measured by the IRAP. Overall, the findings question the validity of the IRAP as a tool for the measurement of absolute implicit attitudes. A new tool (Simple Implicit Procedure:SIP) for measuring absolute, not just relative, implicit attitudes is proposed. (PsycINFO Database Record

  9. wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information

    Science.gov (United States)

    Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

    2016-05-01

    wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users' attention, either because of the size of this information or because some information comes from real - time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users' interests using both, static and real - time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users' interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naïve Bayesian Network which consists of: a) the `decision' random variable corresponding to users' decision on attending an event, b) the `distance' random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ` the seat availability' random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the `relevance' random variable, modeled by a clustering - based collaborative filtering, which determines the relevance of each event users' interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend.

  10. Healthcare information systems: data mining methods in the creation of a clinical recommender system

    Science.gov (United States)

    Duan, L.; Street, W. N.; Xu, E.

    2011-05-01

    Recommender systems have been extensively studied to present items, such as movies, music and books that are likely of interest to the user. Researchers have indicated that integrated medical information systems are becoming an essential part of the modern healthcare systems. Such systems have evolved to an integrated enterprise-wide system. In particular, such systems are considered as a type of enterprise information systems or ERP system addressing healthcare industry sector needs. As part of efforts, nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. We propose to use correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multi-step nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.

  11. A Group Recommendation System for Movies based on MAS

    Directory of Open Access Journals (Sweden)

    Christian Paulo VILLAVICENCIO

    2016-12-01

    Full Text Available Providing recommendations to groups of users has become popular in many applications today. Although several group recommendation techniques exist, the generation of items that satisfy all group members in an even way still remains a challenge. To this end, we have developed a multi-agent approach called PUMAS-GR that relies on negotiation techniques to improve group recommendations. We applied PUMAS-GR to the movies domain, and used the monotonic concession protocol to reach a consensus on the movies proposed to a group.

  12. Use of implicit methods from general sensitivity theory to develop a systematic approach to metabolic control. II. Complex systems.

    Science.gov (United States)

    Cascante, M; Franco, R; Canela, E I

    1989-06-01

    In the accompanying paper (Cascante et al., this issue) we have used general sensitivity theory to develop a matrix algebra that, in the case of sequential reactions, directly relates global and local properties of a given system. In complex biochemical systems this direct relationship is not possible due to the existence of linear dependencies among fluxes and among metabolite concentrations (conserved aggregate concentrations in BST or moiety-conserved concentrations in MCT). In this paper our matrix algebra is applied to conserved cycles and branched pathways, and it is shown that with minor modifications it again relates global properties to the local properties of the enzymes in the system. In the case of conserved cycles, elasticities become modified due to the existence of linear dependencies among the concentration variables in the cycle. In branched pathways, new matrix elements involving ratios of fluxes appear. With these modifications, one can show that the so-called theorems of metabolic control theory specific to these types of pathways are special cases of more general relationships. Rules for the construction of matrices relating global and local properties are given that apply to an arbitrary system of cycles and branches. The implicit approach developed in these papers, which is a generalization of that used in MCT, allows one to make more direct comparisons with the general explicit approach originally developed in BST.

  13. Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios

    Directory of Open Access Journals (Sweden)

    Jesus G. Boticario

    2011-07-01

    Full Text Available This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take advantage of standards-based solutions to provide inclusive support. To this end we have identified the need for developing semantic educational recommender systems, which are able to extend existing learning management systems with adaptive navigation support. In this paper we present three requirements to be considered in developing these semantic educational recommender systems, which are in line with the service-oriented approach of the third generation of learning management systems, namely: (i a recommendation model; (ii an open standards-based service-oriented architecture; and (iii a usable and accessible graphical user interface to deliver the recommendations.

  14. Emoticon Recommendation System to Richen Your Online Communication

    OpenAIRE

    Urabe, Yuki; Rzepka, Rafal; Araki, Kenji

    2014-01-01

    Japanese emoticons are widely used to express users' feelings and intentions in social media, blogs and instant messages. Japanese smartphone keypads have a feature that shows a list of emoticons, enabling users to insert emoticons simply by touching them. However, this list of emoticons contains more than 200, which is difficult to choose from, so a method to reorder the list and recommend appropriate emoticons to users is necessary. This paper proposes an emoticon recommendation method base...

  15. A Semantic Social Recommender System Using Ontologies Based Approach For Tunisian Tourism

    Directory of Open Access Journals (Sweden)

    Mohamed FRIKHA

    2015-12-01

    Full Text Available Tunisia is well placed in terms of medical tourism and has highly qualified and specialized medical and surgical teams. Integrating social networks in Tunisian medical tourism recommender systems can result in much more accurate recommendations. That is to say, information, interests, and recommendations retrieved from social networks can improve the prediction accuracy. This paper aims to improve traditional recommender systems by incorporating information in social network; including user preferences and influences from social friends. Accordingly, a user interest ontology is developed to make personalized recommendations out of such information. In this paper, we present a semantic social recommender system employing a user interest ontology and a Tunisian Medical Tourism ontology. Our system can improve the quality of recommendation for Tunisian tourism domain. Finally, our social recommendation algorithm is implemented in order to be used in a Tunisia tourism Website to assist users interested in visiting Tunisia for medical purposes.

  16. Public reactions to proposed recommendations on management of the national wildlife refuge system

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — The report provides public reactions to proposed recommendations on management of the National Wildlife Refuge System. This report provides the recommendations and...

  17. Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity

    OpenAIRE

    Daniel Fleder; Kartik Hosanagar

    2009-01-01

    This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popular products. This paper is a first attempt to reconcile these seemingly incompatible views. We explore the question in two ways. First, modeling recommender systems analytically allows us to explore...

  18. Implicit Memory in Monkeys: Development of a Delay Eyeblink Conditioning System with Parallel Electromyographic and High-Speed Video Measurements.

    Directory of Open Access Journals (Sweden)

    Yasushi Kishimoto

    Full Text Available Delay eyeblink conditioning, a cerebellum-dependent learning paradigm, has been applied to various mammalian species but not yet to monkeys. We therefore developed an accurate measuring system that we believe is the first system suitable for delay eyeblink conditioning in a monkey species (Macaca mulatta. Monkey eyeblinking was simultaneously monitored by orbicularis oculi electromyographic (OO-EMG measurements and a high-speed camera-based tracking system built around a 1-kHz CMOS image sensor. A 1-kHz tone was the conditioned stimulus (CS, while an air puff (0.02 MPa was the unconditioned stimulus. EMG analysis showed that the monkeys exhibited a conditioned response (CR incidence of more than 60% of trials during the 5-day acquisition phase and an extinguished CR during the 2-day extinction phase. The camera system yielded similar results. Hence, we conclude that both methods are effective in evaluating monkey eyeblink conditioning. This system incorporating two different measuring principles enabled us to elucidate the relationship between the actual presence of eyelid closure and OO-EMG activity. An interesting finding permitted by the new system was that the monkeys frequently exhibited obvious CRs even when they produced visible facial signs of drowsiness or microsleep. Indeed, the probability of observing a CR in a given trial was not influenced by whether the monkeys closed their eyelids just before CS onset, suggesting that this memory could be expressed independently of wakefulness. This work presents a novel system for cognitive assessment in monkeys that will be useful for elucidating the neural mechanisms of implicit learning in nonhuman primates.

  19. Crowd Avoidance and Diversity in Socio-Economic Systems and Recommendation

    CERN Document Server

    Gualdi, Stanislao; Zhang, Yi-Cheng

    2013-01-01

    Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. We address this shortcoming by introducing crowd-avoiding recommendation where each object can be shared by only a limited number of users or where object utility diminishes with the number of users sharing it. We use real data to show that contrary to expectations, the introduction of these constraints enhances recommendation accuracy and diversity even in systems where overcrowding is not detrimental. The observed accuracy improvements are explained in terms of removing potential bias of the recommendation method. We finally propose a way to model artificial socio-economic systems with crowd avoidance and obtain first analytical results.

  20. Crowd avoidance and diversity in socio-economic systems and recommendations

    Science.gov (United States)

    Gualdi, S.; Medo, M.; Zhang, Y.-C.

    2013-01-01

    Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. We address this shortcoming by introducing crowd-avoiding recommendation where each object can be shared by only a limited number of users or where object utility diminishes with the number of users sharing it. We use real data to show that contrary to expectations, the introduction of these constraints enhances recommendation accuracy and diversity even in systems where overcrowding is not detrimental. The observed accuracy improvements are explained in terms of removing potential bias of the recommendation method. We finally propose a way to model artificial socio-economic systems with crowd avoidance and obtain first analytical results.

  1. Capital Controls and Foreign Investor Subsidies Implicit in South Africa's Dual Exchange Rate System

    NARCIS (Netherlands)

    van der Windt, P.C.; Schaling, E.; Huizinga, H.P.

    2007-01-01

    Both in theory and practice, capital controls and dual exchange rate systems can be part of a country's optimal tax policy. We first show how a dual exchange rate system can be interpreted as a tax (or subsidy) on international capital income. We show that a dual exchange rate system, with separate

  2. Building an Internet Radio System with Interdisciplinary factored system for automatic content recommendation

    OpenAIRE

    Wołk, Krzysztof

    2016-01-01

    Automatic systems for music content recommendation have assumed a new role in recent years. These systems have transformed from being just a convenient, standalone tool into an inseparable element of modern living. In addition, not only do these systems strongly influence human moods and feelings with the selection of proper music content, but they also provide significant commercial and advertising opportunities. This research aims to examine and implement two such systems available for the ...

  3. A Geometric Index Reduction Method for Implicit Systems of Differential Algebraic Equations

    CERN Document Server

    D'Alfonso, Lisi; Ollivier, François; Sedoglavic, Alexandre; Solernó, Pablo

    2010-01-01

    This paper deals with the index reduction problem for the class of quasi-regular DAE systems. It is shown that any of these systems can be transformed to a generically equivalent first order DAE system consisting of a single purely algebraic (polynomial) equation plus an under-determined ODE (that is, a semi-explicit DAE system of differentiation index 1) in as many variables as the order of the input system. This can be done by means of a Kronecker-type algorithm with bounded complexity.

  4. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks.

    Science.gov (United States)

    Xiang, Zuoshuang; Qin, Tingting; Qin, Zhaohui S; He, Yongqun

    2013-10-16

    system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.

  5. Social and Behavioral Aspects of a Tag-Based Recommender System

    DEFF Research Database (Denmark)

    Durao, Frederico; Dolog, Peter

    2009-01-01

    Collaborative tagging has emerged as a useful means to organize and share resources on the Web. Recommender systems have been utilized tags for identifying similar resources and generate personalized recommendations. In this paper, we analyze social and behavioral aspects of a tag-based recommender...

  6. Age-related dedifferentiation of learning systems: an fMRI study of implicit and explicit learning

    Science.gov (United States)

    Dennis, Nancy A.; Cabeza, Roberto

    2010-01-01

    Abundant research finds that in young adults explicit learning (EL) is more dependent on the medial temporal lobes (MTL) whereas implicit learning (IL) is more dependent on the striatum. Using fMRI, we investigated age differences in each task and whether this differentiation is preserved in older adults. Results indicated that, while young recruited the MTL for EL and striatum for IL, both activations were significantly reduced in older adults. Additionally, results indicated that older adults recruited the MTL for IL, and this activation was significantly greater in older compared to young adults. A significant Task × Age interaction was found in both regions– with young preferentially recruiting the MTL for EL and striatum for IL, and older adults showing no preferential recruit for either task. Finally, young adults demonstrated significant negative correlations between activity in the striatum and MTL during both the EL and IL tasks. These correlations were attenuated in older adults. Taken together results support dedifferentiation in aging across memory systems. PMID:20471139

  7. Age-related dedifferentiation of learning systems: an fMRI study of implicit and explicit learning.

    Science.gov (United States)

    Dennis, Nancy A; Cabeza, Roberto

    2011-12-01

    Abundant research finds that in young adults explicit learning (EL) is more dependent on the medial temporal lobes (MTL) whereas implicit learning (IL) is more dependent on the striatum. Using fMRI, we investigated age differences in each task and whether this differentiation is preserved in older adults. Results indicated that, while young recruited the MTL for EL and striatum for IL, both activations were significantly reduced in older adults. Additionally, results indicated that older adults recruited the MTL for IL, and this activation was significantly greater in older compared with young adults. A significant Task × Age interaction was found in both regions-with young preferentially recruiting the MTL for EL and striatum for IL, and older adults showing no preferential recruit for either task. Finally, young adults demonstrated significant negative correlations between activity in the striatum and MTL during both the EL and IL tasks. These correlations were attenuated in older adults. Taken together results support dedifferentiation in aging across memory systems. Copyright © 2011 Elsevier Inc. All rights reserved.

  8. Long-Term Effects of Recommendation on the Evolution of Online Systems

    Science.gov (United States)

    Zhao, Dan-Dan; Zeng, An; Shang, Ming-Sheng; Gao, Jian

    2013-11-01

    We employ a bipartite network to describe an online commercial system. Instead of investigating accuracy and diversity in each recommendation, we focus on studying the influence of recommendation on the evolution of the online bipartite network. The analysis is based on two benchmark datasets and several well-known recommendation algorithms. The structure properties investigated include item degree heterogeneity, clustering coefficient and degree correlation. This work highlights the importance of studying the effects and performance of recommendation in long-term evolution.

  9. A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Anna Alphy

    2015-01-01

    Full Text Available In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

  10. A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

    Science.gov (United States)

    Alphy, Anna; Prabakaran, S

    2015-01-01

    In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

  11. The effects of implicit gender role theories on gender system justification: Fixed beliefs strengthen masculinity to preserve the status quo.

    Science.gov (United States)

    Kray, Laura J; Howland, Laura; Russell, Alexandra G; Jackman, Lauren M

    2017-01-01

    Four studies (n = 1199) tested support for the idea that implicit theories about the fixedness versus malleability of gender roles (entity vs. incremental theories) predict differences in the degree of gender system justification, that is, support for the status quo in relations between women and men in society. Relative to an incremental theory, the holding of an entity theory correlated with more system-justifying attitudes and self-perceptions (Study 1) for men and women alike. We also found that strength of identification with one's gender in-group was a stronger predictor of system justification for men than it was for women, suggesting men's defense of the status quo may be motivated by their membership in a high status group in the social hierarchy. In 3 experiments, we then tested whether exposure to a fixed gender role theory would lead men to identify more with masculine characteristics and their male gender group, thus increasing their defense of the gender system as fair and just. We did not expect a fixed gender role theory to trigger these identity-motivated responses in women. Overall, we found that, by increasing the degree of psychological investment in their masculine identity, adopting a fixed gender role theory increased men's rationalization of the gender status quo compared with when gender roles were perceived to be changeable. This suggests that, when men are motivated to align with their masculine identity, they are more likely to endorse the persistence of gender inequality as a way of affirming their status as "real men." (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  12. An implicit Smooth Particle Hydrodynamic code

    Energy Technology Data Exchange (ETDEWEB)

    Knapp, Charles E. [Univ. of New Mexico, Albuquerque, NM (United States)

    2000-05-01

    An implicit version of the Smooth Particle Hydrodynamic (SPH) code SPHINX has been written and is working. In conjunction with the SPHINX code the new implicit code models fluids and solids under a wide range of conditions. SPH codes are Lagrangian, meshless and use particles to model the fluids and solids. The implicit code makes use of the Krylov iterative techniques for solving large linear-systems and a Newton-Raphson method for non-linear corrections. It uses numerical derivatives to construct the Jacobian matrix. It uses sparse techniques to save on memory storage and to reduce the amount of computation. It is believed that this is the first implicit SPH code to use Newton-Krylov techniques, and is also the first implicit SPH code to model solids. A description of SPH and the techniques used in the implicit code are presented. Then, the results of a number of tests cases are discussed, which include a shock tube problem, a Rayleigh-Taylor problem, a breaking dam problem, and a single jet of gas problem. The results are shown to be in very good agreement with analytic solutions, experimental results, and the explicit SPHINX code. In the case of the single jet of gas case it has been demonstrated that the implicit code can do a problem in much shorter time than the explicit code. The problem was, however, very unphysical, but it does demonstrate the potential of the implicit code. It is a first step toward a useful implicit SPH code.

  13. Collaborating Filtering Community Image Recommendation System Based on Scene

    Directory of Open Access Journals (Sweden)

    He Tao

    2017-01-01

    Full Text Available With the advancement of smart city, the development of intelligent mobile terminal and wireless network, the traditional text information service no longer meet the needs of the community residents, community image service appeared as a new media service. “There are pictures of the truth” has become a community residents to understand and master the new dynamic community, image information service has become a new information service. However, there are two major problems in image information service. Firstly, the underlying eigenvalues extracted by current image feature extraction techniques are difficult for users to understand, and there is a semantic gap between the image content itself and the user’s understanding; secondly, in community life of the image data increasing quickly, it is difficult to find their own interested image data. Aiming at the two problems, this paper proposes a unified image semantic scene model to express the image content. On this basis, a collaborative filtering recommendation model of fusion scene semantics is proposed. In the recommendation model, a comprehensiveness and accuracy user interest model is proposed to improve the recommendation quality. The results of the present study have achieved good results in the pilot cities of Wenzhou and Yan'an, and it is applied normally.

  14. Frequency-Weighting Filter Selection for H2 Control of Microgravity Isolation Systems: A Consideration of the "Implicit Frequency Weighting" Problem

    Science.gov (United States)

    Hampton, R. David; Whorton, Mark S.

    2000-01-01

    Many space-science experiments need an active isolation system to provide them with the requisite microgravity environment. The isolation system planned for use with the International Space Station (ISS) have been appropriately modeled using relative position, relative velocity, and acceleration states. In theory, frequency-weighting design filters can be applied to them state-space models, In order to develop optimal H2 or mixed-norm controllers with desired stability and performance characteristics. In practice. however, since there Is a kinematic relationship among the various states. any frequency weighting applied to one state will implicitly weight other states. These implicit frequency-weighting effects must be considered, for intelligent frequency-weighting filter assignment. This paper suggests a rational approach to the assignment of frequency-weighting design filters, in the presence of the kinematic coupling among states that exists in the microgravity vibration isolation problem.

  15. Hermite variational implicit surface reconstruction

    Institute of Scientific and Technical Information of China (English)

    PAN RongJiang; MENG XiangXu; WHANGBO TaegKeun

    2009-01-01

    We propose a new technique for reconstructing surfaces from a large set of unorganized 3D data points and their associated normal vectors. The surface is represented as the zero level set of an implicit vol-ume model which fits the data points and normal constraints. Compared with variational implicit sur-faces, we make use of surface normal vectors at data points directly in the implicit model and avoid of introducing manufactured off-surface points. Given n surface point/normal pairs, the proposed method only needs to solve an n×n positive definite linear system. It allows fitting large datasets effectively and robustly. We demonstrate the performance of the proposed method with both globally supported and compactly supported radial basis functions on several datasets.

  16. Can implicit motivation be measured?

    DEFF Research Database (Denmark)

    Kraus, Alexandra; Scholderer, Joachim

    such promising conceptual frameworks within consumer research would not only be helpful for understanding human appetite but also has implications for predicting consumer behaviour. Since the affective liking system has strong similarities to contemporary attitude theories, implicit and explicit measures...... of evaluation could be applied. However, no comparable procedures have been developed for the motivational wanting component; generally accepted “low-tech” measures are therefore still lacking! Thus, the aim of this study was to develop and test implicit measures of wanting that can be used as dependent...... variables in consumer and sensory research. Modified versions of three IAT paradigms were developed, including the conventional implicit association test (IAT) and two recent modifications, the single-block IAT (SB-IAT) and the recoding-free IAT (IAT-RF). All three tests were designed to measure...

  17. Approximate Implicitization Using Linear Algebra

    Directory of Open Access Journals (Sweden)

    Oliver J. D. Barrowclough

    2012-01-01

    Full Text Available We consider a family of algorithms for approximate implicitization of rational parametric curves and surfaces. The main approximation tool in all of the approaches is the singular value decomposition, and they are therefore well suited to floating-point implementation in computer-aided geometric design (CAGD systems. We unify the approaches under the names of commonly known polynomial basis functions and consider various theoretical and practical aspects of the algorithms. We offer new methods for a least squares approach to approximate implicitization using orthogonal polynomials, which tend to be faster and more numerically stable than some existing algorithms. We propose several simple propositions relating the properties of the polynomial bases to their implicit approximation properties.

  18. Implicit nucleophilic reagents in the sulfur dioxide-amine system. Sulfur trioxide treatment of azomethines

    Energy Technology Data Exchange (ETDEWEB)

    Bodrikov, I.V.; Krasnov, V.L.; Matyukov, E.V.; Chernov, A.N.; Verin, I.A.

    1988-03-20

    It was shown by x-ray crystallographic analysis that the products form the reaction of azomethines with the sulfur dioxide-methylamine system are methylammonium 1-aryl-1-(arylamino)methanesulfonates. In aqueous solutions of the latter the amine fragment is redistributed with the formation of arylammonium 1-aryl-1-(arylamino)methanesulfonates. It was established that all the obtained salts are in equilibrium with the azomethines in solutions. The form of the nucleophile taking part in the reaction with the azomethine is suggested on the basis of the data from the PMR spectra of the methylamine-sulfur dioxide system. It was established that in water at room temperature and, particularly, with acid catalysis the compounds undergo fragment transformations which lead finally to redistribution of the covalently bonded amine fragment between the anionic and cationic parts of the salt with the formation of arylammonium 1-aryl-1-(arylamino)methanesulfonates.

  19. Unconditionally Energy Stable Implicit Time Integration: Application to Multibody System Analysis and Design

    DEFF Research Database (Denmark)

    Chen, Shanshin; Tortorelli, Daniel A.; Hansen, John Michael

    1999-01-01

    however yields spurious oscillations in the computed accelerations. Therefore, a new acceleration correction is applied to eliminate these instabilities and hence retain unconditional stability in an energy sense. In addition sensitivity analyisis and optimizations are applied to create a mechanism design......Advances in computer hardware and improved algorithms for multibody dynamics over the past decade have generated widespread interest in real-time simulations of multibody mechanics systems. At the heart of the widely used algorithms for multibody dynamics are a choice of coordinates which define...... tool. To exemplify the methodology, a wheel loader mechanism is designed to minimize energy consumption subject to trajectory constraints....

  20. Image Content in Location-Based Shopping Recommender Systems For Mobile Users

    Directory of Open Access Journals (Sweden)

    Tranos Zuva

    2012-08-01

    Full Text Available This paper shows how image content can be used to realize a shopping recommender system for intuitively supporting mobile users in decision making. A mobile user equipped with a camera enabled smart phone combined with Global Positioning System (GPS capabilities would benefit in using a recommender system for mobile users. This recommender system is queried by image sent by a smart phone together with the smart phone’s GPS coordinates then the system returns a recommended retail shop together with its GPS coordinates, the image similar to the query image and other items on special offer. This recommender system shows a drastic reduction if not elimination of usage of text by mobileusers using mobile devices when accessing the system. This paper presents the proposed recommender system and the simulated results of the recommender system. In summary the main contribution of this paper is to show how image retrieval, image content and camera enabled smart mobile device with GPS capabilities can be used to realize a location-based shopping recommender system for mobile users.

  1. A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment

    Science.gov (United States)

    Bambini, Riccardo; Cremonesi, Paolo; Turrin, Roberto

    In this chapter we describe the integration of a recommender system into the production environment of Fastweb, one of the largest European IP Television (IPTV) providers. The recommender system implements both collaborative and content-based techniques, suitable tailored to the specific requirements of an IPTV architecture, such as the limited screen definition, the reduced navigation capabilities, and the strict time constraints. The algorithms are extensively analyzed by means of off-line and on-line tests, showing the effectiveness of the recommender systems: up to 30% of the recommendations are followed by a purchase, with an estimated lift factor (increase in sales) of 15%.

  2. AN ONTOLOGY-BASED TOURISM RECOMMENDER SYSTEM BASED ON SPREADING ACTIVATION MODEL

    Directory of Open Access Journals (Sweden)

    Z. Bahramian

    2015-12-01

    Full Text Available A tourist has time and budget limitations; hence, he needs to select points of interest (POIs optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user’s preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontology-based recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User’s feedback adapts the user’s preferences using Spreading Activation (SA strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.

  3. Parallel explicit and implicit control of reaching.

    Directory of Open Access Journals (Sweden)

    Pietro Mazzoni

    Full Text Available BACKGROUND: Human movement can be guided automatically (implicit control or attentively (explicit control. Explicit control may be engaged when learning a new movement, while implicit control enables simultaneous execution of multiple actions. Explicit and implicit control can often be assigned arbitrarily: we can simultaneously drive a car and tune the radio, seamlessly allocating implicit or explicit control to either action. This flexibility suggests that sensorimotor signals, including those that encode spatially overlapping perception and behavior, can be accurately segregated to explicit and implicit control processes. METHODOLOGY/PRINCIPAL FINDINGS: We tested human subjects' ability to segregate sensorimotor signals to parallel control processes by requiring dual (explicit and implicit control of the same reaching movement and testing for interference between these processes. Healthy control subjects were able to engage dual explicit and implicit motor control without degradation of performance compared to explicit or implicit control alone. We then asked whether segregation of explicit and implicit motor control can be selectively disrupted by studying dual-control performance in subjects with no clinically manifest neurologic deficits in the presymptomatic stage of Huntington's disease (HD. These subjects performed successfully under either explicit or implicit control alone, but were impaired in the dual-control condition. CONCLUSION/SIGNIFICANCE: The human nervous system can exert dual control on a single action, and is therefore able to accurately segregate sensorimotor signals to explicit and implicit control. The impairment observed in the presymptomatic stage of HD points to a possible crucial contribution of the striatum to the segregation of sensorimotor signals to multiple control processes.

  4. A New Profile Learning Model for Recommendation System based on Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Shereen H. Ali

    2016-03-01

    Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.

  5. Consumers' intention to use health recommendation systems to receive personalized nutrition advice

    NARCIS (Netherlands)

    S. Wendel (Sonja); B.G.C. Dellaert (Benedict); A. Ronteltap (Amber); H.C.M. van Trijp (Hans)

    2013-01-01

    textabstractBackground: Sophisticated recommendation systems are used more and more in the health sector to assist consumers in healthy decision making. In this study we investigate consumers' evaluation of hypothetical health recommendation systems that provide personalized nutrition advice. We exa

  6. How Recommender Systems in Technology-Enhanced Learning depend on Context

    NARCIS (Netherlands)

    Drachsler, Hendrik; Manouselis, Nikos

    2009-01-01

    Drachsler, H., & Manouselis, N. (2009). How Recommender Systems in Technology-Enhanced Learning depend on Context. Presentation given at the 1st workshop on Context-aware Recommender Systems for Learning at the Alpine Rendez-Vous 2009. November, 30 - December, 3, 2009, Garmisch-Patenkirchen, Germany

  7. Applying Web Usage Mining Techniques to Design Effective Web Recommendation Systems: A Case Study

    Directory of Open Access Journals (Sweden)

    Maryam Jafari

    Full Text Available Recommender systems are helpful tools which provide an adaptive Web environment for Web users. Recently, a number of Web page recommender systems have been developed to extract the user behavior from the user’s navigational path and predict the next reque ...

  8. Hardware performance assessment recommendations and tools for baropodometric sensor systems

    Directory of Open Access Journals (Sweden)

    Claudia Giacomozzi

    2010-06-01

    Full Text Available Accurate plantar pressure measurements are mandatory in both clinical and research contexts. Differences in accuracy, precision, reliability of pressure measurement devices (PMDs prevented so far the onset of standardization processes and of reliable reference datasets. The Italian National Institute of Health (ISS approved and conducted a scientific project aimed to design, validate and implement dedicated testing methods for both in-factory and on-the-field PMD assessment. A general-purpose experimental set-up was built, complete and suitable for the assessment of PMDs based on different sensor technology, electronic conditioning and mechanical solutions. Preliminary assessments have been conducted on 5 commercial PMDs. The study lead to the definition of: i an appropriate set of instruments and procedures for PMD technical assessment; ii a minimum set of significant parameters for the technical characterization of the PMD performance; iii some recommendations to both manufacturers and end users for an appropriate use in clinics and in research context

  9. PRS: PERSONNEL RECOMMENDATION SYSTEM FOR HUGE DATA ANALYSIS USING PORTER STEMMER

    Directory of Open Access Journals (Sweden)

    T N Chiranjeevi

    2016-04-01

    Full Text Available Personal recommendation system is one which gives better and preferential recommendation to the users to satisfy their personalized requirements such as practical applications like Webpage Preferences, Sport Videos preferences, Stock selection based on price, TV preferences, Hotel preferences, books, Mobile phones, CDs and various other products now use recommender systems. The existing Pearson Correlation Coefficient (PCC and item-based algorithm using PCC, are called as UPCC and IPCC respectively. These systems are mainly based on only the rating services and does not consider the user personal preferences, they simply just give the result based on the ratings. As the size of data increases it will give the recommendations based on the top rated services and it will miss out most of user preferences. These are main drawbacks in the existing system which will give same results to the users based on some evaluations and rankings or rating service, they will neglect the user preferences and necessities. To address this problem we propose a new approach called, Personnel Recommendation System (PRS for huge data analysis using Porter Stemmer to solve the above challenges. In the proposed system it provides a personalized service recommendation list to the users and recommends the most useful services to the users which will increase the accuracy and efficiency in searching better services. Particularly, a set of suggestions or keywords are provided to indicate user preferences and we used Collaborative Filtering and Porter Stemmer algorithm which gives a suitable recommendations to the users. In real, the broad experiments are conducted on the huge database which is available in real world, and outcome shows that our proposed personal recommender method extensively improves the precision and efficiency of service recommender system over the KASR method. In our approach mainly consider the user preferences so it will not miss out the any of the data

  10. The Impact of Channel Context and Task on Consumers' Evaluations of Personalized Health Recommendation Systems

    NARCIS (Netherlands)

    Wendel, S.; Ronteltap, A.; Dellaert, B.G.C.; Trijp, van J.C.M.

    2008-01-01

    We investigate consumer perspectives on complex, multistage systems designed to provide personalized health recommendations. We conceptualize the underlying benefit trade-offs that consumers make in evaluating such systems as the manifestation of a psychological contract in which consumers contribut

  11. Combining content and relation analysis for recommendation in social tagging systems

    Science.gov (United States)

    Zhang, Yin; Zhang, Bin; Gao, Kening; Guo, Pengwei; Sun, Daming

    2012-11-01

    Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.

  12. A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

    Directory of Open Access Journals (Sweden)

    Pei-Chann Chang

    2016-07-01

    Full Text Available This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

  13. Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

    CERN Document Server

    Amini, Bahram; Othman, Mohd Shahizan

    2011-01-01

    Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.

  14. Tutoring the Elderly on the Use of Recommending Systems

    Science.gov (United States)

    Savvopoulos, Anastasios; Virvou, Maria

    2010-01-01

    Purpose: The elderly are often unfamiliar with computer technology and can encounter great difficulties. Moreover, the terms used in such systems may prove to be a challenge for these users. The aim of this research is to tutor the elderly on using an adaptive e-shop system in order to buy products easily. Design/methodology/approach: In view of…

  15. Tutoring the Elderly on the Use of Recommending Systems

    Science.gov (United States)

    Savvopoulos, Anastasios; Virvou, Maria

    2010-01-01

    Purpose: The elderly are often unfamiliar with computer technology and can encounter great difficulties. Moreover, the terms used in such systems may prove to be a challenge for these users. The aim of this research is to tutor the elderly on using an adaptive e-shop system in order to buy products easily. Design/methodology/approach: In view of…

  16. Personalized Recommender System for e-Learning Environment

    Science.gov (United States)

    Benhamdi, Soulef; Babouri, Abdesselam; Chiky, Raja

    2017-01-01

    Traditional e-Learning environments are based on static contents considering that all learners are similar, so they are not able to respond to each learner's needs. These systems are less adaptive and once a system that supports a particular strategy has been designed and implemented, it is less likely to change according to student's interactions…

  17. Using Fuzzy Association Rules to Design E-commerce Personalized Recommendation System

    OpenAIRE

    Guofang Kuang; Yuanchen Li

    2013-01-01

    In order to improve the efficiency of fuzzy association rule mining, the paper defines the redundant fuzzy association rules, and strong fuzzy association rules redundant nature. As much as possible for more information in the e-commerce environment, and in the right form is a prerequisite for personalized recommendation. Personalized recommendation technology is a core issue of e-commerce automated recommendation system. Higher complexity than ordinary association rules algorithm fuzzy assoc...

  18. The Impact of Comments and Recommendation System on Online Shopper Buying Behaviour

    OpenAIRE

    Hui Chen

    2012-01-01

    The author aims at studying the influence of comments and recommendation on online shopping behaviours. With 285 study subjects, the author used experimental research design to study comments and recommendation system on online shopping experience, online shopping satisfaction, online shopping intention and items chosen. The data is analyzed by SPSS 15.0 and LISREL 8.8. The results show that comments and recommendation influence online shopping experience, online shopping satisfaction and onl...

  19. The Impact of Comments and Recommendation System on Online Shopper Buying Behaviour

    OpenAIRE

    Hui Chen

    2012-01-01

    The author aims at studying the influence of comments and recommendation on online shopping behaviours. With 285 study subjects, the author used experimental research design to study comments and recommendation system on online shopping experience, online shopping satisfaction, online shopping intention and items chosen. The data is analyzed by SPSS 15.0 and LISREL 8.8. The results show that comments and recommendation influence online shopping experience, online shopping satisfaction and onl...

  20. Designing and Developing a Novel Hybrid Adaptive Learning Path Recommendation System (ALPRS) for Gamification Mathematics Geometry Course

    Science.gov (United States)

    Su, Chung-Ho

    2017-01-01

    Since recommendation systems possess the advantage of adaptive recommendation, they have gradually been applied to e-learning systems to recommend subsequent learning content for learners. However, problems exist in current learning recommender systems available to students in that they are often general learning content and unable to offer…

  1. Isotopic Implicit Surface Meshing

    NARCIS (Netherlands)

    Boissonnat, Jean-Daniel; Cohen-Steiner, David; Vegter, Gert

    2004-01-01

    This paper addresses the problem of piecewise linear approximation of implicit surfaces. We first give a criterion ensuring that the zero-set of a smooth function and the one of a piecewise linear approximation of it are isotopic. Then, we deduce from this criterion an implicit surface meshing algor

  2. Implicit Theories of Persuasion.

    Science.gov (United States)

    Roskos-Ewoldsen, David R.

    1997-01-01

    Explores whether individuals have implicit theories of persuasion. Examines how persuasive strategies are cognitively represented--identifies types of tactics in attitude change and social acceptability of persuasive strategies. Finds implicit theories of persuasion reflect the audience's familiarity with the topic. Finds also that implicit…

  3. When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System

    KAUST Repository

    Liu, Xiao

    2017-03-21

    Privacy risks of recommender systems have caused increasing attention. Users’ private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users’ private data. Existing approaches focus either on keeping users’ private data protected during recommendation computation or on preventing the inference of any single user’s data from the recommendation result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.

  4. Round table part 6: Recommendations for system studies

    Science.gov (United States)

    Lasseur, Christophe; Tikhomirov, Alexander A.; Wheeler, Raymond; Dussap, Gilles; Godia, Francesc

    2016-07-01

    Depending of each mission scenario and associated requirements the ECLSS architecture will have to be studied and elaborated. Depending of the degree of closure and mission duration, which are often link the degree of stability and robustness will be became crucial. It is clear today that if the metrics exists on many space agencies ( e.g. ESM, ALiSSE,..), the sub-systems process often miss a minimum degree of characterization requested for proper system model. These part 6 is aiming to be a platform of discussion on the current world wide investigations related to system studies and to allow synergies and collaborations.

  5. Comparison of Different Fishery Statistical Systems and Recommendations for China

    Institute of Scientific and Technical Information of China (English)

    Jiahua; LE; Zhengyi; SHAO

    2015-01-01

    With the integration of global economy and rapid development of information technology,China’s economic and trade exchange will be further strengthened and social economic phenomenon will become more and more complex. Therefore,understanding fishery statistical systems and making comparative analysis become particularly important for formulating fishery development and economic management policies.Through comparative study on statistical systems,organization,statistical laws and regulations,statistical indicators,and statistical management and methods of different countries,this paper is intended to provide reference for improving China’s fishery statistical system and operating mechanism.

  6. Diagnosis and recommender system for some neglected tropical ...

    African Journals Online (AJOL)

    International Journal of Natural and Applied Sciences ... The implementation of the system had a front- end web based graphical user interface (GUI) application and ... NET (using vb.net-programming language), while the back-end was ...

  7. Implicit finite-difference simulations of seismic wave propagation

    KAUST Repository

    Chu, Chunlei

    2012-03-01

    We propose a new finite-difference modeling method, implicit both in space and in time, for the scalar wave equation. We use a three-level implicit splitting time integration method for the temporal derivative and implicit finite-difference operators of arbitrary order for the spatial derivatives. Both the implicit splitting time integration method and the implicit spatial finite-difference operators require solving systems of linear equations. We show that it is possible to merge these two sets of linear systems, one from implicit temporal discretizations and the other from implicit spatial discretizations, to reduce the amount of computations to develop a highly efficient and accurate seismic modeling algorithm. We give the complete derivations of the implicit splitting time integration method and the implicit spatial finite-difference operators, and present the resulting discretized formulas for the scalar wave equation. We conduct a thorough numerical analysis on grid dispersions of this new implicit modeling method. We show that implicit spatial finite-difference operators greatly improve the accuracy of the implicit splitting time integration simulation results with only a slight increase in computational time, compared with explicit spatial finite-difference operators. We further verify this conclusion by both 2D and 3D numerical examples. © 2012 Society of Exploration Geophysicists.

  8. Framing (implicitly) matters

    DEFF Research Database (Denmark)

    Anderson, Joel; Antalikova, Radka

    2014-01-01

    Denmark is currently experiencing the highest immigration rate in its modern history. Population surveys indicate that negative public attitudes toward immigrants actually stem from attitudes toward their (perceived) Islamic affiliation. We used a framing paradigm to investigate the explicit...... and implicit attitudes of Christian and Atheist Danes toward targets framed as Muslims or as immigrants. The results showed that explicit and implicit attitudes were more negative when the target was framed as a Muslim, rather than as an immigrant. Interestingly, implicit attitudes were qualified...... by the participants’ religion. Specifically, analyses revealed that Christians demonstrated more negative implicit attitudes toward immigrants than Muslims. Conversely, Atheists demonstrated more negative implicit attitudes toward Muslims than Atheists. These results suggest a complex relationship between religion...

  9. On the Use of a Wider Class of Linear Systems for the Design of Constant-Coefficients Semi-Implicit Time-Schemes in NWP

    CERN Document Server

    Benard, P

    2003-01-01

    The linearization of the meteorological equations around a specified reference state, usually applied in NWP to define the linear system of constant-coefficients semi-implicit schemes, is outlined as an unnecessarily restrictive approach which may be detrimental in terms of stability. It is shown theoretically that an increased robustness can sometimes be obtained by choosing the reference linear system in a wider set of possibilities. The potential benefits of this new approach are illustrated in two simple examples. The advantage in robustness is not obtained at the price of an increased error or complexity.

  10. Twin Cities care system assessment: process, findings, and recommendations.

    Science.gov (United States)

    Othieno, Joan

    2007-08-01

    The Twin Cities Care system lacks services that are most needed in the later stages of HIV disease. Services in highest demand included housing, transportation, and translation; available translations services are generally limited to Somali, Oromo, and Amharic, the languages most widely spoken by the three largest African immigrant and refugee groups in the Twin Cities. The care system is not well-integrated, and most of the work of moving clients within the system is done by case managers and care advocates. The main technical competencies identified by providers as lacking are understanding mental health from the perspective of African-born people living with HIV/AIDS (PLWH) and addressing sexual issues, especially with women. African providers with foreign certifications not recognized in the United States are not able to use their professional skills. African clients are not well-informed about HIV, and African women are more likely than men to seek and stay in care.

  11. SemCiR: A Citation Recommendation System Based on a Novel Semantic Distance Measure

    Science.gov (United States)

    Zarrinkalam, Fattane; Kahani, Mohsen

    2013-01-01

    Purpose: The purpose of this paper is to propose a novel citation recommendation system that inputs a text and recommends publications that should be cited by it. Its goal is to help researchers in finding related works. Further, this paper seeks to explore the effect of using relational features in addition to textual features on the quality of…

  12. District cooling pipes. Pipes and components in district cooling systems. Technical recommendations

    Energy Technology Data Exchange (ETDEWEB)

    2009-07-15

    Euroheat and Power (EHP) draws up technical recommendations for pipes and components in district heating and district cooling systems. Through references to these requirements, the quality of products and systems is ensured and procurement and installation are facilitated. The recommendations are based on experiences, standards, development and research results. These recommendations cover only the type of pipes and materials listed in the table of content. Material such as reinforced plastic AP, glass fibre, reinforced plastic GAP or nodular iron can be used but they are not in the scope of this recommendation. These recommendations are meant for DC systems using treated water with quality values comparable to DH water. As these requirements include different materials and solutions, the customer should make active selections when procuring a system. Full column wide text in these technical recommendations includes requirements, while indented text is informative. The tables presented in this set of recommendations are based on Swedish experience. The Task Force Transport and Distribution at Euroheat and Power has drawn up these technical recommendations

  13. Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

    Directory of Open Access Journals (Sweden)

    Ronaldo Lima Rocha Campos

    2012-07-01

    Full Text Available This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.

  14. An E-Commerce Recommender System Based on Content-Based Filtering

    Institute of Scientific and Technical Information of China (English)

    HE Weihong; CAO Yi

    2006-01-01

    Content-based filtering E-commerce recommender system was discussed fully in this paper. Users' unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products information, the recommender lists were obtained. Since the system can adapt to the users' feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.

  15. An Instructional and Collaborative Learning System with Content Recommendation

    Science.gov (United States)

    Zheng, Xiang-wei; Ma, Hong-wei; Li, Yan

    2013-01-01

    With the rapid development of Internet, e-learning has become a new teaching and learning mode. However, lots of e-learning systems deployed on Internet are just electronic learning materials with very limited interactivity and diagnostic capability. This paper presents an integrated e-learning environment named iCLSR. Firstly, iCLSR provides an…

  16. WIPP shaft seal system parameters recommended to support compliance calculations

    Energy Technology Data Exchange (ETDEWEB)

    Hurtado, L.D.; Knowles, M.K. [Sandia National Labs., Albuquerque, NM (United States); Kelley, V.A.; Jones, T.L.; Ogintz, J.B. [INTERA Inc., Austin, TX (United States); Pfeifle, T.W. [RE/SPEC, Inc., Rapid City, SD (United States)

    1997-12-01

    The US Department of Energy plans to dispose of transuranic waste at the Waste Isolation Pilot Plant (WIPP), which is sited in southeastern New Mexico. The WIPP disposal facility is located approximately 2,150 feet (650 m) below surface in the bedded halite of the Salado Formation. Prior to initiation of disposal activities, the Department of Energy must demonstrate that the WIPP will comply with all regulatory requirements. Applicable regulations require that contaminant releases from the WIPP remain below specified levels for a period of 10,000 years. To demonstrate that the WIPP will comply with these regulations, the Department of Energy has requested that Sandia National Laboratories develop and implement a comprehensive performance assessment of the WIPP repository for the regulatory period. This document presents the conceptual model of the shaft sealing system to be implemented in performance assessment calculations conducted in support of the Compliance Certification Application for the WIPP. The model was developed for use in repository-scale calculations and includes the seal system geometry and materials to be used in grid development as well as all parameters needed to describe the seal materials. These calculations predict the hydrologic behavior of the system. Hence conceptual model development is limited to those processes that could impact the fluid flow through the seal system.

  17. Modelling of Thermal Advective Reactive Flow in Hydrothermal Mineral Systems Using an Implicit Time-stepped Finite Element Method.

    Science.gov (United States)

    Hornby, P. G.

    2005-12-01

    Understanding chemical and thermal processes taking place in hydrothermal mineral deposition systems could well be a key to unlocking new mineral reserves through improved targeting of exploration efforts. To aid in this understanding it is very helpful to be able to model such processes with sufficient fidelity to test process hypotheses. To gain understanding, it is often sufficient to obtain semi-quantitative results that model the broad aspects of the complex set of thermal and chemical effects taking place in hydrothermal systems. For example, it is often sufficient to gain an understanding of where thermal, geometric and chemical factors converge to precipitate gold (say) without being perfectly precise about how much gold is precipitated. The traditional approach is to use incompressible Darcy flow together with the Boussinesq approximation. From the flow field, the heat equation is used to advect-conduct the heat. The flow field is also used to transport solutes by solving an advection-dispersion-diffusion equation. The reactions in the fluid and between fluid and rock act as source terms for these advection-dispersion equations. Many existing modelling systems that are used for simulating such systems use explicit time marching schemes and finite differences. The disadvantage of this approach is the need to work on rectilinear grids and the number of time steps required by the Courant condition in the solute transport step. The second factor can be particularly significant if the chemical system is complex, requiring (at a minimum) an equilibrium calculation at each grid point at each time step. In the approach we describe, we use finite elements rather than finite differences, and the pressure, heat and advection-dispersion equations are solved implicitly. The general idea is to put unconditional numerical stability of the time integration first, and let accuracy assume a secondary role. It is in this sense that the method is semi-quantiative. However

  18. Tag-Aware Recommender Systems: A State-of-the-art Survey

    CERN Document Server

    Zhang, Zi-Ke; Zhang, Yi-Cheng; 10.1007/s11390-011-0176-1

    2012-01-01

    In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.

  19. Towards Small-Sized Long Tail Business with the Dual-Directed Recommendation System

    Science.gov (United States)

    Takahashi, Masakazu; Yamada, Takashi; Tsuda, Kazuhiko; Terano, Takao

    This paper describes a novel architecture to promote retail businesses using information recommendation systems. The main features of the architecture are 1) Dual-directed Recommendation system, 2) Portal site for three kinds of users: Producers, Retailers, and Consumers, which are considered to be Prosumers, and 3) Agent-based implementation. We have developed a web-based system DAIKOC (Dynamic Advisor for Information and Knowledge Oriented Communities) with the above architecture. In this paper, we focus on the recommendation functions to extract the items that will achieve the large sales in the future from the ID (IDentification)-POS (Point-Of-Sales) data.

  20. A method for predicting errors when interacting with finite state systems. How implicit learning shapes the user's knowledge of a system

    Energy Technology Data Exchange (ETDEWEB)

    Javaux, Denis

    2002-02-01

    This paper describes a method for predicting the errors that may appear when human operators or users interact with systems behaving as finite state systems. The method is a generalization of a method used for predicting errors when interacting with autopilot modes on modern, highly computerized airliners [Proc 17th Digital Avionics Sys Conf (DASC) (1998); Proc 10th Int Symp Aviat Psychol (1999)]. A cognitive model based on spreading activation networks is used for predicting the user's model of the system and its impact on the production of errors. The model strongly posits the importance of implicit learning in user-system interaction and its possible detrimental influence on users' knowledge of the system. An experiment conducted with Airbus Industrie and a major European airline on pilots' knowledge of autopilot behavior on the A340-200/300 confirms the model predictions, and in particular the impact of the frequencies with which specific state transitions and contexts are experienced.

  1. Management approach recommendations. Earth Observatory Satellite system definition study (EOS)

    Science.gov (United States)

    1974-01-01

    Management analyses and tradeoffs were performed to determine the most cost effective management approach for the Earth Observatory Satellite (EOS) Phase C/D. The basic objectives of the management approach are identified. Some of the subjects considered are as follows: (1) contract startup phase, (2) project management control system, (3) configuration management, (4) quality control and reliability engineering requirements, and (5) the parts procurement program.

  2. The study of personalized information recommendation system based on data mining

    Science.gov (United States)

    Chen, Ke; Ke, Wende; Li, Sansi

    2011-12-01

    For the current Internet information access of contradictions and difficulties, the study on the basis of the data mining technique and recommender system, propose and implement a facing internet personalization information recommendation system based on data mining. The system is divided into offline and online, offline part to complete the from the site server log files access the appropriate online intelligent personalized recommendation service transaction mode, using the association rules mining. The online part, realizes personalized intelligence recommendation service based on the connection rule excavation. Provides the personalization information referral service method based mining association rules, And through the experiment to this system has carried on the test, has confirmed this system's feasibility and the validity.

  3. PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

    Directory of Open Access Journals (Sweden)

    Wei Jiang

    2016-01-01

    Full Text Available The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

  4. Aircraft Hydraulic System Leakage Detection and Servicing Recommendations Method

    Science.gov (United States)

    2014-10-02

    accumulators, filters, and consumers, that include all the actuators connected to the hydraulic power such as flight controls , brake and landing...Conference, October 4-8 Calgary, Alberta, Canada. Merrit, H. E., (1967), Hydraulic Control Systems. New York: John Willey & Sons. Vianna, W. O. L...2008), Modelagem e Análise do Sistema Hidráulico de uma Aeronave Comercial Regional. M.Sc. Thesis. Instituto Tecnológico de Aeronáutica, São José

  5. Long-term Mechanical Circulatory Support System reliability recommendation by the National Clinical Trial Initiative subcommittee.

    Science.gov (United States)

    Lee, James

    2009-01-01

    The Long-Term Mechanical Circulatory Support (MCS) System Reliability Recommendation was published in the American Society for Artificial Internal Organs (ASAIO) Journal and the Annals of Thoracic Surgery in 1998. At that time, it was stated that the document would be periodically reviewed to assess its timeliness and appropriateness within 5 years. Given the wealth of clinical experience in MCS systems, a new recommendation has been drafted by consensus of a group of representatives from the medical community, academia, industry, and government. The new recommendation describes a reliability test methodology and provides detailed reliability recommendations. In addition, the new recommendation provides additional information and clinical data in appendices that are intended to assist the reliability test engineer in the development of a reliability test that is expected to give improved predictions of clinical reliability compared with past test methods. The appendices are available for download at the ASAIO journal web site at www.asaiojournal.com.

  6. An Intelligent Multi-Agent Recommender System for Human Capacity Building

    CERN Document Server

    Marivate, Vukosi N; Marwala, Tshilidzi

    2008-01-01

    This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together in recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.

  7. Awareness of Implicit Attitudes

    Science.gov (United States)

    Hahn, Adam; Judd, Charles M.; Hirsh, Holen K.; Blair, Irene V.

    2013-01-01

    Research on implicit attitudes has raised questions about how well people know their own attitudes. Most research on this question has focused on the correspondence between measures of implicit attitudes and measures of explicit attitudes, with low correspondence interpreted as showing that people have little awareness of their implicit attitudes. We took a different approach and directly asked participants to predict their results on upcoming IAT measures of implicit attitudes toward five different social groups. We found that participants were surprisingly accurate in their predictions. Across four studies, predictions were accurate regardless of whether implicit attitudes were described as true attitudes or culturally learned associations (Studies 1 and 2), regardless of whether predictions were made as specific response patterns (Study 1) or as conceptual responses (Studies 2–4), and regardless of how much experience or explanation participants received before making their predictions (Study 4). Study 3 further suggested that participants’ predictions reflected unique insight into their own implicit responses, beyond intuitions about how people in general might respond. Prediction accuracy occurred despite generally low correspondence between implicit and explicit measures of attitudes, as found in prior research. All together, the research findings cast doubt on the belief that attitudes or evaluations measured by the IAT necessarily reflect unconscious attitudes. PMID:24294868

  8. Concept of "One Window" Data Exchange System Fulfilling the Recommendation for e-Navigation System

    Science.gov (United States)

    Filipkowski, Damian; Wawruch, Ryszard

    The implementation in maritime radio-communication of so called "One window concept" for exchange of information between a ship and a port and coastal state authorities requires designation of one contact point on shore for these purposes, e.g. harbour master or ships' monitoring or traffic control centre. In Poland, as contact points regional and local centres of the Polish National Maritime Safety System will be designated. Paper describes the proposal for system of data exchange between a ship and a shore contact point, containing definition, functions and architecture of proposed system, possible directions of information flow and levels of access, fulfilling requirements of this concept and recommendation for e-navigation system developed on the basis of the International Maritime Organization (IMO) and International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) working papers.

  9. Recommended Liquid-Liquid Equilibrium Data. Hydrocarbons with Seawater Systems

    Science.gov (United States)

    Góral, Marian; Gierycz, Paweł; Oracz, Paweł; Shaw, David G.

    2011-12-01

    The solubilities of C5-C26 hydrocarbons in seawater, reviewed previously, were re-evaluated using a predictive model based on the Sechenov equation. It was found that, within the scope of investigated data, the Sechenov constant is proportional to a hydrocarbon-specific parameter representing the size of the cavity in water needed to accommodate the dissolved molecule of the hydrocarbon. The proportionality coefficient has one value for n-alkanes, cycloalkanes, and alkylbenzenes, whereas for higher aromatics (including those with fused rings), a second value of the coefficient is indicated. The proposed model provides a framework for comparison of the data for various systems and helps in the recognition of systematic error. Evaluation of experimental solubility data and analysis of error propagation is given.

  10. IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

    Directory of Open Access Journals (Sweden)

    Yahya AlMurtadha

    2011-01-01

    Full Text Available Problem statement: Recently, Web usage mining techniques have been widely used to build recommendation systems especially for anonymous users. Approach: Assigning the current user to the best web navigation profile with similar navigation activities will improve the ability of the prediction engine to produce a recommendation list then introduce it to the user. This study presents iPACT an improved recommendation system using Profile Aggregation based on Clustering of Transactions (PACT. Results: iPACT shows better prediction accuracy than the previous methods PACT and Hypergraph. Conclusion: The users interests change over time; hence an incremental and adaptive web navigation profiling is a key feature for the future works.

  11. Recommender System Based on Algorithm of Bicluster Analysis RecBi

    CERN Document Server

    Ignatov, Dmitry I; Zaharchuk, Vasily

    2012-01-01

    In this paper we propose two new algorithms based on biclustering analysis, which can be used at the basis of a recommender system for educational orientation of Russian School graduates. The first algorithm was designed to help students make a choice between different university faculties when some of their preferences are known. The second algorithm was developed for the special situation when nothing is known about their preferences. The final version of this recommender system will be used by Higher School of Economics.

  12. Recommendation System for Product Using User Interest, Social Circle and Location of User

    Directory of Open Access Journals (Sweden)

    Himgauri D. Ambulkar , Apashabi Pathan

    2016-07-01

    Full Text Available Recommendation System (RS is used to discover users interested items. The present testimonial framework is not efficient as desire. It has to require enhancement in framework for current and future necessities to getting best results for recommendation qualities. This paper combines four factors such as users interest, personal interest similarity, interpersonal impact and user’s location information. In propose system we add user location in dataset also use PCC similarity method which reduce the RMSE and MAE errors

  13. 推荐系统研究综述%A Survey of Recommender System

    Institute of Scientific and Technical Information of China (English)

    李斌

    2014-01-01

    In recent years, recommender system gets unprecedented attention and development. As the core of e-commerce technology, the recom-mender system helps consumers easily find potential commodity and also promotes the sale of goods for consumers and producers. Recom-mender system can utilize user behavior information, social network information, tag data, etc. to enhance the quality of the recommender system. With the rapid development of the recommender system, how to evaluate the system and how to improve the interpretability rec-ommendation system has also become a hot research topic. Makes detailed introduction and analysis of recommendation algorithm, evalua-tion indicators and interpretability of recommender system.%近年来,推荐系统得到前所未有的关注和发展。作为电子商务的核心技术,推荐系统在帮助消费者便捷的找到所需的潜在商品同时也促进商品的销售,对于消费者和商品生产者来说都至关重要。推荐系统可以利用用户的行为信息、社交网络信息、标签数据等来提升推荐系统的质量。随着推荐系统的快速发展,如何评价推荐系统以及如何提高推荐系统的可解释性也成为热门的研究课题。从推荐算法、评测指标和可解释性三个部分对推荐系统的研究现状进行分析总结。

  14. A Deployed People-to-People Recommender System in Online Dating

    OpenAIRE

    2015-01-01

    Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market. This article reports on the successful deployment of a people-to-people recommender system on a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. R...

  15. Simulating light-weight Personalised Recommender Systems in Learning Networks: A case for Pedagogy-Oriented and Rating-based Hybrid Recommendation Strategies

    NARCIS (Netherlands)

    Nadolski, Rob; Van den Berg, Bert; Berlanga, Adriana; Drachsler, Hendrik; Hummel, Hans; Koper, Rob; Sloep, Peter

    2008-01-01

    Nadolski, R. J., Van den Berg, B., Berlanga, A. J., Drachsler, H., Hummel, H. G. K., Koper, R., & Sloep, P. B. (2009). Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies. Journal of Artificial

  16. An Efficient System Based On Closed Sequential Patterns for Web Recommendations

    Directory of Open Access Journals (Sweden)

    Utpala Niranjan

    2010-05-01

    Full Text Available Sequential pattern mining, since its introduction has received considerable attention among the researchers with broad applications. The sequential pattern algorithms generally face problems when mining long sequential patterns or while using very low support threshold. One possible solution of such problems is by mining the closed sequential patterns, which is a condensed representation of sequential patterns. Recently, several researchers have utilized the sequential pattern discovery for designing a web recommendation system, which provides personalized recommendations of web access sequences for users. This paper describes the design of a web recommendation system for providing recommendations to a user's web access sequence. The proposed system is mainly based on mining closed sequential web access patterns. Initially, the PrefixSpan algorithm is employed on the preprocessed web server log data for mining sequential web access patterns. Subsequently, with the aid of post-pruning strategy, the closed sequential web access patterns are discovered from the complete set of sequential web access patterns. Then, a pattern tree, a compact representation of closed sequential patterns, is constructed from the discovered closed sequential web access patterns. The Patricia trie based data structure is used in the construction of the pattern tree. For a given user's web access sequence, the proposed system provides recommendations on the basis of the constructed pattern tree. The experimentation of the proposed system is performed using synthetic dataset and the performance of the proposed recommendation system is evaluated with precision, applicability and hit ratio.

  17. [GRADE system: classification of quality of evidence and strength of recommendation].

    Science.gov (United States)

    Aguayo-Albasini, José Luis; Flores-Pastor, Benito; Soria-Aledo, Víctor

    2014-02-01

    The acquisition and classification of scientific evidence, and subsequent formulation of recommendations constitute the basis for the development of clinical practice guidelines. There are several systems for the classification of evidence and strength of recommendations; the most commonly used nowadays is the Grading of Recommendations, Assessment, Development and Evaluation system (GRADE). The GRADE system initially classifies the evidence into high or low, coming from experimental or observational studies; subsequently and following a series of considerations, the evidence is classified into high, moderate, low or very low. The strength of recommendations is based not only on the quality of the evidence, but also on a series of factors such as the risk/benefit balance, values and preferences of the patients and professionals, and the use of resources or costs.

  18. Using Recommendation System for E-learning Environments at degree level

    Directory of Open Access Journals (Sweden)

    Rubén González Crespo

    2009-12-01

    Full Text Available Nowadays, new technologies and the fast growth of the Internet have made access to information easier for all kind of people, raising new challenges to education when using Internet as a medium. One of the best examples is how to guide students in their learning processes.The need to look for guidance from their teachers or other companions that many Internet users experience when endeavoring to choose their readings, exercises o practices is a very common reality. In order to cater for this need many different information and recommendation strategies have been developed. Recommendation Systems is one of these.Recommendation Systems try to help the user, presenting him those objects he could be more interested in, based on his known preferences or on those of other users with similar characteristics.This document tries to present the current situation with regards to Recommendation Systems and their application on distance education over the Internet.

  19. Intelligent Knowledge Recommendation Methods for R&D Knowledge Portals

    Institute of Scientific and Technical Information of China (English)

    KIM Jongwoo; LEE Hongjoo; PARK Sungjoo

    2004-01-01

    The personalization in knowledge portals and knowledge management systems is mainly performed based on users' explicitly specified categories and keywords. The explicit specification approach requires users' participation to start personalization services, and has limitation to adapt changes of users' preference. This paper suggests two implicit personalization approaches: automatic user category assignment method and automatic keyword profile generation method. The performances of the implicit personalization approaches are compared with traditional personalization approach using an Internet news site experiment. The result of the experiment shows that the suggested personalization approaches provide sufficient recommendation effectiveness with lessening users'unwanted involvement in personalization process.

  20. Implicit and explicit ethnocentrism: revisiting the ideologies of prejudice.

    Science.gov (United States)

    Cunningham, William A; Nezlek, John B; Banaji, Mahzarin R

    2004-10-01

    Two studies investigated relationships among individual differences in implicit and explicit prejudice, right-wing ideology, and rigidity in thinking. The first study examined these relationships focusing on White Americans' prejudice toward Black Americans. The second study provided the first test of implicit ethnocentrism and its relationship to explicit ethnocentrism by studying the relationship between attitudes toward five social groups. Factor analyses found support for both implicit and explicit ethnocentrism. In both studies, mean explicit attitudes toward out groups were positive, whereas implicit attitudes were negative, suggesting that implicit and explicit prejudices are distinct; however, in both studies, implicit and explicit attitudes were related (r = .37, .47). Latent variable modeling indicates a simple structure within this ethnocentric system, with variables organized in order of specificity. These results lead to the conclusion that (a) implicit ethnocentrism exists and (b) it is related to and distinct from explicit ethnocentrism.

  1. Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media

    Directory of Open Access Journals (Sweden)

    Basma AlBanna

    2016-12-01

    Full Text Available Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs and to the emergence of a newer class called Location-Based Social Networks (LBSNs. While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR, which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1 utilizing the geo-content in both LBSNs and SNs; (2 ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness.

  2. Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

    Science.gov (United States)

    Wang, Wei; Wang, Hongwei

    2015-10-01

    The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.

  3. Shilling Attack Prevention for Recommender Systems Using Social-based Clustering

    KAUST Repository

    Lee, Tak

    2011-06-06

    A Recommender System (RS) is a system that utilizes user and item information to predict the feeling of users towards unfamiliar items. Recommender Systems have become popular tools for online stores due to their usefulness in confidently recommending items to users. A popular algorithm for recommender system is Collaborative Filtering (CF). CF uses other users\\' profiles to predict whether a user is interested in a particular object. This system, however, is vulnerable to malicious users seeking to promote items by manipulating rating predictions with fake user profiles. Profiles with behaviors similar to "victim" users alter the prediction of a Recommender System. Manipulating rating predictions through injected profiles is referred to as a shilling attack. It is important to develop shilling attack prevention frameworks for to protect the trustworthiness of Recommender Systems. In this thesis, we will demonstrate a new methodology that utilizes social information to prevent malicious users from manipulating the prediction system. The key element in our new methodology rests upon the concept of trust among real users, an element we claim absent among malicious profiles. In order to use trust information for shilling attack prevention, we first develop a weighting system which makes the system rely more on trustworthy users when making predictions. We then use this trust information to cluster out untrustworthy users to improve rating robustness. The robustness of the new and classic systems is then evaluated with data from a public commercial consumer RS, Epinions.com. Several complexity reduction procedures are also introduced to make implementing the algorithms mentioned possible for a huge commercial database.

  4. A scalable and practical one-pass clustering algorithm for recommender system

    Science.gov (United States)

    Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali

    2015-12-01

    KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.

  5. The Impact of Comments and Recommendation System on Online Shopper Buying Behaviour

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2012-02-01

    Full Text Available The author aims at studying the influence of comments and recommendation on online shopping behaviours. With 285 study subjects, the author used experimental research design to study comments and recommendation system on online shopping experience, online shopping satisfaction, online shopping intention and items chosen. The data is analyzed by SPSS 15.0 and LISREL 8.8. The results show that comments and recommendation influence online shopping experience, online shopping satisfaction and online shopping intention directly. Online shopping experience and online shopping satisfaction influence online shopping intention significantly. There is remarkable positive relation between online shopping intention and items chosen.

  6. Using Fuzzy Association Rules to Design E-commerce Personalized Recommendation System

    Directory of Open Access Journals (Sweden)

    Guofang Kuang

    2013-09-01

    Full Text Available In order to improve the efficiency of fuzzy association rule mining, the paper defines the redundant fuzzy association rules, and strong fuzzy association rules redundant nature. As much as possible for more information in the e-commerce environment, and in the right form is a prerequisite for personalized recommendation. Personalized recommendation technology is a core issue of e-commerce automated recommendation system. Higher complexity than ordinary association rules algorithm fuzzy association rules, the low efficiency become a bottleneck in the practical application of fuzzy association rules algorithm. The paper presents using fuzzy association rules to design E-commerce personalized recommendation system. The experimental results show that the new algorithm to improve the efficiency of the implementation.

  7. Using Contextual Information as Virtual Items on Top-N Recommender Systems

    CERN Document Server

    Domingues, Marcos A; Soares, Carlos

    2011-01-01

    Traditionally, recommender systems for the Web deal with applications that have two dimensions, users and items. Based on access logs that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a method to complement the information in the access logs with contextual information without changing the recommendation algorithm. The method consists in representing context as virtual items. We empirically test this method with two top-N recommender systems, an item-based collaborative filtering technique and association rules, on three data sets. The results show that our method is able to take advantage of the context (new dimensions) when it is informative.

  8. A Mobile Ecotourism Recommendations System Using Cars-Context Aware Approaches

    Directory of Open Access Journals (Sweden)

    Yani Nurhadryani

    2013-11-01

    Full Text Available The requirements to fullfill mobility of ecotourism activities have been urgent to support each traveler with the mobile gadget application. The objective of this research is to develop an application of recommendation system based on online user personalization. This application provided features to recommendation of ecotourism based on profile user and current location, then supplied information about distance and facilities in each ecotourism place. The main of computation worked online which was based on approach called as CARS (Context Aware Recommender Systems algorithm. The result showed that the application framework succeeded to give appropriate recommendations and explaination on a mobile platform both in the form of profile based spatial data and user preferences.

  9. Application of Recommended Design Practices for Conceptual Nuclear Fusion Space Propulsion Systems

    Science.gov (United States)

    Williams, Craig H.

    2004-01-01

    An AIAA Special Project Report was recently produced by AIAA's Nuclear and Future Flight Propulsion Technical Committee and is currently in peer review. The Report provides recommended design practices for conceptual engineering studies of nuclear fusion space propulsion systems. Discussion and recommendations are made on key topics including design reference missions, degree of technological extrapolation and concomitant risk, thoroughness in calculating mass properties (nominal mass properties, weight-growth contingency and propellant margins, and specific impulse), and thoroughness in calculating power generation and usage (power-flow, power contingencies, specific power). The report represents a general consensus of the nuclear fusion space propulsion system conceptual design community and proposes 15 recommendations. This paper expands on the Report by providing specific examples illustrating how to apply each of the recommendations.

  10. A new Recommender system based on target tracking: a Kalman Filter approach

    CERN Document Server

    Nowakowski, Samuel; Boyer, Anne

    2010-01-01

    In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.

  11. The Design and Implementation of an Intelligent Apparel Recommend Expert System

    Directory of Open Access Journals (Sweden)

    A. H. Dong

    2013-01-01

    Full Text Available Now with the rapid development of information science and technology, intelligent apparel recommend has drawn wide attention in apparel retail industry. Intelligent management and effective recommend are two issues of crucial importance for the retail store to enhance its corporate influence and increase its economic benefits. This paper proposes an intelligent recommend system design scheme for apparel retail which is based on expert system. By comprehensive utilization of database management and expert system technology, the proposed system provides a solid solution in improving the customer shopping experience. This paper presents a kind of object-oriented blackboard structure, which is applied in the apparel recommend expert system and establishes expert rule on the basis of apparel characteristic elements. Through the establishment of the rule base, the system generates personal recommend list by positive rule reasoning mechanism engine. The proposed method thus gives dress collocation scheme for the customer through the human-machine interaction from the point of view of the apparel experts. This design scheme avails the customers to experience targeted service with intellectualization, and personalization and it has certain reference significance for promoting apparel retail intelligence development.

  12. Survey of personalized recommendation system%个性化推荐系统综述

    Institute of Scientific and Technical Information of China (English)

    王国霞; 刘贺平

    2012-01-01

    信息超载是目前网络用户面临的一个严重问题,个性化推荐系统是解决该问题的一个有力工具,并受到了众多的关注和研究.给出推荐系统的定义,同时阐述了推荐系统的几项关键技术,包括用户建模、推荐对象的建模和推荐算法.后来总结了推荐系统的体系结构和性能评价指标,并尝试给出了推荐系统未来研究的重点、难点和热点问题.%Information overload is one of the most critical problems, and personalized recommendation system is a powerful tool to solve this problem. In this article, the definition of recommendation system is introduced, this article also expounds some key technologies including user modeling, recommendation item modeling and recommendation algorithm. The recommendation frame and evaluation methods are also exhibited. This article tries to give the difficulties and future directions of recommendation system.

  13. Recommender System:Up to Now%推荐系统研究进展

    Institute of Scientific and Technical Information of China (English)

    朱扬勇; 孙婧

    2015-01-01

    Recommender system is the product of cyber age today. There have been many achievements in research and application. This paper makes a comprehensive survey of the recommender system. It proposes three research phases, and points out the milestone events in each stage of recommender system development. In the age of big data, exploiting recommendation in the perspective of data, this paper classifies the recommender system into seven main classes according to the different data used in recommendation, and analyzes and comments the recommended models used in each classification and their advantages and disadvantages. Exploiting big data in the perspective of recom-mendation, this paper proposes that making recommendation based on big data is one of the promising research direc-tions. Finally, this paper compares the evaluation metrics of recommendation, and gives future research directions.%推荐系统(recommender system,RS)是当今网络时代的产物,在技术研究和应用方面取得了很多成果。综述了推荐系统领域的研究状况和进展,提出了3个研究阶段,并指出了每个阶段标志性意义的事件。在当前大数据环境下,从数据的角度看推荐,提出了推荐系统新的分类方法,即根据推荐时所使用的数据不同分为7种类别,同时指出了每个类别使用了哪些推荐模型及其优缺点。提出了在大数据环境下进行推荐是未来推荐系统研究的一个大方向,分析了推荐视角下的大数据机制。最后比较和总结了推荐系统的评价指标,给出了未来的主要研究方向和可能的突破点。

  14. [Secondary school menu in Madrid (Spain): knowledge and adherence to the Spanish National Health System recommendations].

    Science.gov (United States)

    Berradre-Sáenz, Belén; Royo-Bordonada, Miguel Ángel; Bosqued, María José; Moya, María Ángeles; López, Lázaro

    2015-01-01

    To establish the degree of knowledge and adherence to the Spanish National Health System recommendations on nutrition in schools in the Autonomous Community of Madrid. Cross-sectional study of a random sample of 182 secondary schools from Madrid, during 2013-2014 school year. Information on the characteristics of the schools and the knowledge of the recommendations was collected by internet and telephone interviews, as well as a copy of the school menu. The average number of rations per week offered for each food item and the percentage of schools within the recommended range were calculated. The overall adherence was obtained as the mean of food items (0-12) within the range. 65.5% of the schools were unaware of the national recommendations. The supply of rice, pasta, fish, eggs, salad and fruit was lower than recommended, whereas for meat, accompaniment and other desserts was higher. The percentage of schools within the range for each food item varied between 13% and 95%. The mean of overall adherence was 6.3, with no differences depending on whether the menu was prepared or not at schools or there was or not a person in charge of nutrition standards. The degree of adherence to the recommendations was variable, being advised to increase the supply of cereals, eggs, fish, salad and fruit. Programs for dissemination and implementation of the recommendations, leaded by trained professionals, are required to improve the nutritional value of school menu. Copyright © 2015 SESPAS. Published by Elsevier Espana. All rights reserved.

  15. Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

    Science.gov (United States)

    Verbert, K.; Manouselis, N.; Ochoa, X.; Wolpers, M.; Drachsler, H.; Bosnic, I.; Duval, E.

    2012-01-01

    Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely…

  16. Recommender System for E-Learning Based on Semantic Relatedness of Concepts

    Directory of Open Access Journals (Sweden)

    Mao Ye

    2015-08-01

    Full Text Available Digital publishing resources contain a lot of useful and authoritative knowledge. It may be necessary to reorganize the resources by concepts and recommend the related concepts for e-learning. A recommender system is presented in this paper based on the semantic relatedness of concepts computed by texts from digital publishing resources. Firstly, concepts are extracted from encyclopedias. Information in digital publishing resources is then reorganized by concepts. Secondly, concept vectors are generated by skip-gram model and semantic relatedness between concepts is measured according to the concept vectors. As a result, the related concepts and associated information can be recommended to users by the semantic relatedness for learning or reading. History data or users’ preferences data are not needed for recommendation in a specific domain. The technique may not be language-specific. The method shows potential usability for e-learning in a specific domain.

  17. A novel video recommendation system based on efficient retrieval of human actions

    Science.gov (United States)

    Ramezani, Mohsen; Yaghmaee, Farzin

    2016-09-01

    In recent years, fast growth of online video sharing eventuated new issues such as helping users to find their requirements in an efficient way. Hence, Recommender Systems (RSs) are used to find the users' most favorite items. Finding these items relies on items or users similarities. Though, many factors like sparsity and cold start user impress the recommendation quality. In some systems, attached tags are used for searching items (e.g. videos) as personalized recommendation. Different views, incomplete and inaccurate tags etc. can weaken the performance of these systems. Considering the advancement of computer vision techniques can help improving RSs. To this end, content based search can be used for finding items (here, videos are considered). In such systems, a video is taken from the user to find and recommend a list of most similar videos to the query one. Due to relating most videos to humans, we present a novel low complex scalable method to recommend videos based on the model of included action. This method has recourse to human action retrieval approaches. For modeling human actions, some interest points are extracted from each action and their motion information are used to compute the action representation. Moreover, a fuzzy dissimilarity measure is presented to compare videos for ranking them. The experimental results on HMDB, UCFYT, UCF sport and KTH datasets illustrated that, in most cases, the proposed method can reach better results than most used methods.

  18. A CONTEXT-AWARE TOURISM RECOMMENDER SYSTEM BASED ON A SPREADING ACTIVATION METHOD

    Directory of Open Access Journals (Sweden)

    Z. Bahramian

    2017-09-01

    Full Text Available Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized recommendations based on the user’s preferences. It also incorporates contextual information to improve the recommendation process. As previous context-aware tourism recommender systems suffer from a lack of formal definition to represent contextual information and user’s preferences, the proposed system is enhanced using an ontology approach. We also apply a spreading activation technique to contextualize user preferences and learn the user profile dynamically according to the user’s feedback. The proposed method assigns more effect in the spreading process for nodes which their preference values are assigned directly by the user. The results show the overall performance of the proposed context-aware tourism recommender systems by an experimental application to the city of Tehran.

  19. Design Context Aware Activity Recommender System for Iranian Customer Mind Activism in Online Shopping

    Directory of Open Access Journals (Sweden)

    Saber Pahlavan

    2013-11-01

    Full Text Available E-commerce has made life simple and innovative of individuals and groups. Nowadays, social networks are widely used by everyone. So, it is necessary to do appropriate and situation aware activities in these networks to gain benefits, In this research, a context aware recommender system has modeled for using in social networks focus on Iranian customer mind activism in online shopping. This system makes its recommendations for user based on behavior and activities of her friends in the same situation in social network. In other word, this modeled recommender system uses collaborative filtering algorithm. All the connections of user in social network, containing direct and indirect, are considered for recommending by recommender system; but, based on connection type and its distance to user, proportional factor is assigned. On the other, In this research we study the consumer behavior in online shopping of electronics especially in Iran. Primary data was collected through the questionnaire survey and by emails from personal contacts in two major cities of Iran. Price, time saving and convenience were identified as important factors which lead to certain buying behavior in online shopping.

  20. Multi-agent system for Knowledge-based recommendation of Learning Objects

    Directory of Open Access Journals (Sweden)

    Paula Andrea RODRÍGUEZ MARÍN

    2015-12-01

    Full Text Available Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

  1. Wind energy Computerized Maintenance Management System (CMMS) : data collection recommendations for reliability analysis.

    Energy Technology Data Exchange (ETDEWEB)

    Peters, Valerie A.; Ogilvie, Alistair; Veers, Paul S.

    2009-09-01

    This report addresses the general data requirements for reliability analysis of fielded wind turbines and other wind plant equipment. The report provides a list of the data needed to support reliability and availability analysis, and gives specific recommendations for a Computerized Maintenance Management System (CMMS) to support automated analysis. This data collection recommendations report was written by Sandia National Laboratories to address the general data requirements for reliability analysis of fielded wind turbines. This report is intended to help the reader develop a basic understanding of what data are needed from a Computerized Maintenance Management System (CMMS) and other data systems, for reliability analysis. The report provides: (1) a list of the data needed to support reliability and availability analysis; and (2) specific recommendations for a CMMS to support automated analysis. Though written for reliability analysis of wind turbines, much of the information is applicable to a wider variety of equipment and a wider variety of analysis and reporting needs.

  2. Tag-Aware Recommender Systems: A State-of-the-Art Survey

    Institute of Scientific and Technical Information of China (English)

    Zi-Ke Zhang; Tao Zhou; Yi-Cheng Zhang

    2011-01-01

    In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been addressed to unify tagging information to reveal user behaviors and preferences,extract the latent semantic relations among items,make recommendations,and so on.Specifically,this article summarizes recent progress about tag-aware recommender systems,emphasizing on the contributions from three mainstream perspectives and approaches:network-based methods,tensor-based methods,and the topic-based methods.Finally,we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.

  3. Electromagnetic pulse research on electric power systems: Program summary and recommendations. Power Systems Technology Program

    Energy Technology Data Exchange (ETDEWEB)

    Barnes, P.R.; McConnell, B.W.; Van Dyke, J.W. [Oak Ridge National Lab., TN (United States); Tesche, F.M. [Tesche (F.M.), Dallas, TX (United States); Vance, E.F. [Vance (E.F.), Fort Worth, TX (United States)

    1993-01-01

    A single nuclear detonation several hundred kilometers above the central United States will subject much of the nation to a high-altitude electromagnetic pulse (BENT). This pulse consists of an intense steep-front, short-duration transient electromagnetic field, followed by a geomagnetic disturbance with tens of seconds duration. This latter environment is referred to as the magnetohydrodynamic electromagnetic pulse (NMENT). Both the early-time transient and the geomagnetic disturbance could impact the operation of the nation`s power systems. Since 1983, the US Department of Energy has been actively pursuing a research program to assess the potential impacts of one or more BENT events on the nation`s electric energy supply. This report summarizes the results of that program and provides recommendations for enhancing power system reliability under HENT conditions. A nominal HENP environment suitable for assessing geographically large systems was developed during the program and is briefly described in this report. This environment was used to provide a realistic indication of BEMP impacts on electric power systems. It was found that a single high-altitude burst, which could significantly disturb the geomagnetic field, may cause the interconnected power network to break up into utility islands with massive power failures in some areas. However, permanent damage would be isolated, and restoration should be possible within a few hours. Multiple bursts would likely increase the blackout areas, component failures, and restoration time. However, a long-term blackout of many months is unlikely because major power system components, such as transformers, are not likely to be damaged by the nominal HEND environment. Moreover, power system reliability, under both HENT and normal operating conditions, can be enhanced by simple, and often low cost, modifications to current utility practices.

  4. E-book recommender system design and implementation based on data mining

    Science.gov (United States)

    Wang, Zongjiang

    2011-12-01

    In the knowledge explosion, rapid development of information age, how quickly the user or users interested in useful information for feedback to the user problem to be solved in this article. This paper based on data mining, association rules to the model and classification model a combination of electronic books on the recommendation of the user's neighboring users interested in e-books to target users. Introduced the e-book recommendation and the key technologies, system implementation algorithms, and implementation process, was proved through experiments that this system can help users quickly find the required e-books.

  5. Implicit measure for yoga research: Yoga implicit association test

    Directory of Open Access Journals (Sweden)

    Judu V Ilavarasu

    2014-01-01

    Conclusions: Implicit measures may be used in the yoga field to assess constructs, which are difficult to self-report or may have social desirability threat. Y-IAT may be used to evaluate implicit preference toward yoga.

  6. Implicit stage topics

    Directory of Open Access Journals (Sweden)

    Karen Lahousse

    2008-04-01

    Full Text Available Il a souvent été proposé que les éléments spatio-temporels en position initiale de phrase spécifient le cadre de l’événement dénoté par la proposition et ont une interprétation thématique ou topicale. Alors que les topiques spatio-temporels explicites ont souvent été étudiés, Erteschik-Schir (1997, 1999 propose l’idée que les topiques spatio-temporels, ou topiques scéniques (stage topics peuvent aussi être implicites.Dans cet article, nous offrons des arguments en faveur de la notion de topique scénique implicite. Nous montrons qu’un certain nombre de cas d’inversion nominale en français, une configuration syntaxique qui est favorisée par la présence d’un topique scénique explicite, s’expliquent par la présence d’un topique scénique implicite. Le fait que les topiques scéniques implicites interagissent avec la structure syntaxique de la même façon que les topiques scéniques explicites constitue un argument empirique en faveur de leur existence.It has often been proposed that sentence-initial spatio-temporal elements specify the frame in which the whole proposition takes place and are topical (i.e. thematic. Whereas considerable attention has been paid to explicit spatio-temporal topics, Erteschik-Shir (1997, 1999 argues that spatio-temporal topics, or stage topics, can also be implicit.In this article we provide evidence in favour of the notion of implicit stage topic. We show that a certain number of nominal inversion cases in French, a syntactic configuration which is triggered by the presence of an explicit stage topic, are explained by the presence of an implicit stage topic. The fact that implicit stage topics interact with syntactic structure the same way explicit stage topics do constitutes a strong empirical argument in favour of their existence.

  7. Mindfulness - en implicit utopi?

    DEFF Research Database (Denmark)

    Nielsen, Anne Maj

    2014-01-01

    mindfulness is used for individualized stress-reduction in order to keep up with existing or worsened working conditions instead of stress-reducing changes in the common working conditions. Mindfulness research emphasizes positive outcomes in coping with demands and challenges in everyday life especially...... considering suffering (for example stress and pain). While explicit constructions of Utopia present ideas of specific societal communities in well-functioning harmony, the interest in mindfulness can in contradistinction be considered an implicit critique of present life-conditions and an “implicit utopia...

  8. Application of Content-Based Approach in Research Paper Recommendation System for a Digital Library

    Directory of Open Access Journals (Sweden)

    Simon Philip

    2014-10-01

    Full Text Available Recommender systems are software applications that provide or suggest items to intended users. These systems use filtering techniques to provide recommendations. The major ones of these techniques are collaborative-based filtering technique, content-based technique, and hybrid algorithm. The motivation came as a result of the need to integrate recommendation feature in digital libraries in order to reduce information overload. Content-based technique is adopted because of its suitability in domains or situations where items are more than the users. TF-IDF (Term Frequency Inverse Document Frequency and cosine similarity were used to determine how relevant or similar a research paper is to a user's query or profile of interest. Research papers and user's query were represented as vectors of weights using Keyword-based Vector Space model. The weights indicate the degree of association between a research paper and a user's query. This paper also presents an algorithm to provide or suggest recommendations based on users' query. The algorithm employs both TF-IDF weighing scheme and cosine similarity measure. Based on the result or output of the system, integrating recommendation feature in digital libraries will help library users to find most relevant research papers to their needs.

  9. Computer equipment used in patient care within a multihospital system: recommendations for cleaning and disinfection.

    Science.gov (United States)

    Neely, Alice N; Weber, Joan M; Daviau, Patricia; MacGregor, Alastair; Miranda, Carlos; Nell, Marie; Bush, Patricia; Lighter, Donald

    2005-05-01

    Computer hardware has been implicated as a potential reservoir for infectious agents. Leaders of a 22-hospital system, which spans North America and serves pediatric patients with orthopedic or severe burns, sought to develop recommendations for the cleaning and disinfection of computer hardware within its myriad patient care venues. A task force comprising representatives from infection control, medical affairs, information services, and outcomes management departments was formed. Following a review of the literature and of procedures within the 22 hospitals, criteria for cleaning and disinfection were established and recommendations made. The recommendations are consistent with general environmental infection control cleaning and disinfection guidelines, yet flexible enough to be applicable to the different locales, different computer and cleaning products available, and different patient populations served within this large hospital system.

  10. Solving the Cold-Start Problem in Recommender Systems with Social Tags

    CERN Document Server

    Liu, Zi-Ke Zhang Chuang; Zhou, Tao

    2010-01-01

    In this paper, based on the user-tag-object tripartite graphs, we propose a recommendation algorithm, which considers social tags as an important role for information retrieval. Besides its low cost of computational time, the experiment results of two real-world data sets, \\emph{Del.icio.us} and \\emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity. Especially, it can obtain more personalized recommendation results when users have diverse topics of tags. In addition, the numerical results on the dependence of algorithmic accuracy indicates that the proposed algorithm is particularly effective for small degree objects, which reminds us of the well-known \\emph{cold-start} problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree distributions.

  11. Tailoring NIST Security Controls for the Ground System: Selection and Implementation -- Recommendations for Information System Owners

    Science.gov (United States)

    Takamura, Eduardo; Mangum, Kevin

    2016-01-01

    . Certain protective measures for the general enterprise may not be as efficient within the ground segment. This is what the authors have concluded through observations and analysis of patterns identified from the various security assessments performed on NASA missions such as MAVEN, OSIRIS-REx, New Horizons and TESS, to name a few. The security audits confirmed that the framework for managing information system security developed by the National Institute of Standards and Technology (NIST) for the federal government, and adopted by NASA, is indeed effective. However, the selection of the technical, operational and management security controls offered by the NIST model - and how they are implemented - does not always fit the nature and the environment where the ground system operates in even though there is no apparent impact on mission success. The authors observed that unfit controls, that is, controls that are not necessarily applicable or sufficiently effective in protecting the mission systems, are often selected to facilitate compliance with security requirements and organizational expectations even if the selected controls offer minimum or non-existent protection. This paper identifies some of the standard security controls that can in fact protect the ground system, and which of them offer little or no benefit at all. It offers multiple scenarios from real security audits in which the controls are not effective without, of course, disclosing any sensitive information about the missions assessed. In addition to selection and implementation of controls, the paper also discusses potential impact of recent legislation such as the Federal Information Security Modernization Act (FISMA) of 2014 - aimed at the enterprise - on the ground system, and offers other recommendations to Information System Owners (ISOs).

  12. Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

    Directory of Open Access Journals (Sweden)

    Zhengyou Xia

    2014-01-01

    Full Text Available The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs. In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

  13. Hot news recommendation system from heterogeneous websites based on bayesian model.

    Science.gov (United States)

    Xia, Zhengyou; Xu, Shengwu; Liu, Ningzhong; Zhao, Zhengkang

    2014-01-01

    The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

  14. Long-term effects of user preference-oriented recommendation method on the evolution of online system

    Science.gov (United States)

    Shi, Xiaoyu; Shang, Ming-Sheng; Luo, Xin; Khushnood, Abbas; Li, Jian

    2017-02-01

    As the explosion growth of Internet economy, recommender system has become an important technology to solve the problem of information overload. However, recommenders are not one-size-fits-all, different recommenders have different virtues, making them be suitable for different users. In this paper, we propose a novel personalized recommender based on user preferences, which allows multiple recommenders to exist in E-commerce system simultaneously. We find that output of a recommender to each user is quite different when using different recommenders, the recommendation accuracy can be significantly improved if each user is assigned with his/her optimal personalized recommender. Furthermore, different from previous works focusing on short-term effects on recommender, we also evaluate the long-term effect of the proposed method by modeling the evolution of mutual feedback between user and online system. Finally, compared with single recommender running on the online system, the proposed method can improve the accuracy of recommendation significantly and get better trade-offs between short- and long-term performances of recommendation.

  15. DESIGN AND DEVELOPMENT OF A RECOMMENDER SYSTEM FOR E-LEARNING MODULES

    Directory of Open Access Journals (Sweden)

    Baseera

    2014-01-01

    Full Text Available Extensive literature review revealed that, different recommender systems for E-learning were developed. A preliminary version of the development was undertaken and evaluated in an experiment during an introduction psychology course in an open university. The activities were integrated in to a model which operates on a network. No curriculum structure was assigned and the users were allowed to undergo learning activities in any order they wanted. Though voluminious work was reported on establishing bench marks for learning process on a wide range basis, there are only discrete references on developing a system comprising of a web-based typical learning environment which includes many aspects of learning such as course content delivery tools, synchronous and asynchronous conferencing systems, quiz models, grade reporting systems, creation of virtual class rooms. An attempt is made in this study to design and develop a Recommender System (RS, in the form of a software agent giving recommendations based on the previous observations. The developed RS suggests the applications of web mining techniques resulting in, on-line learning activities and improving the course material navigation. The proposed RS combines a top down ontology based recommendation techniques clubbed with bottom-up techniques. Both techniques were combined in the RS, which decided, which of the techniques is more suitable for the current situation in which a learner works. Finally the present work provides for recommendation strategies for a personal RS in E-learning models for life long learners. The major contribution of the present work lies, in designing and developing a RS in the form of a software agent, incorporating web mining techniques resulting in, on-line learning activities, such as course content delivery tools, conferencing systems, creation of virtual class rooms.

  16. USER RECOMMENDATION ALGORITHM IN SOCIAL TAGGING SYSTEM BASED ON HYBRID USER TRUST

    Directory of Open Access Journals (Sweden)

    Norwati Mustapha

    2013-01-01

    Full Text Available With the rapid growth of web 2.0 technologies, tagging become much more important today to facilitate personal organization and also provide a possibility for users to search information or discover new things with Collaborative Tagging Systems. However, the simplistic and user-centered design of this kind of systems cause the task of finding personally interesting users is becoming quite out of reach for the common user. Collaborative Filtering (CF seems to be the most popular technique in recommender systems to deal with information overload issue but CF suffers from accuracy limitation. This is because CF always been at-tack by malicious users that will make it suffers in finding the truly interesting users. With this problem in mind, this study proposes a hybrid User Trust method to enhance CF in order to increase accuracy of user recommendation in social tagging system. This method is a combination of developing trust network based on user interest similarity and trust network from social network analysis. The user interest similarity is de-rived from personalized user tagging information. The hybrid User Trust method is able to find the most trusted users and selected as neighbours to generate recommendations. Experimental results show that the hybrid method outperforms the traditional CF algorithm. In addition, it indicated that the hybrid method give more accurate recommendation than the existing CF based on user trust.

  17. Effective Trust-Aware E-learning Recommender System Based on Learning Styles and Knowledge Levels

    Science.gov (United States)

    Dwivedi, Pragya; Bharadwaj, Kamal K.

    2013-01-01

    In the age of information explosion, e-learning recommender systems (ELRSs) have emerged as the most essential tool to deliver personalized learning resources to learners. Due to enormous amount of information on the web, learner faces problem in searching right information. ELRSs deal with the problem of information overload effectively and…

  18. Design and Realization of user Behaviors Recommendation System Based on Association rules under Cloud Environment

    Directory of Open Access Journals (Sweden)

    Wei Dai

    2013-07-01

    Full Text Available This study introduces the basal principles of association rules, properties and advantages of Map Reduce model and Hbase in Hadoop ecosystem. And giving design steps of the user's actions recommend system in detail, many time experiences proves that the exploration combined association rules theory with cloud computing is successful and effective.

  19. Effects of the ISIS Recommender System for Navigation Support in Self-Organized Learning Networks

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Van den Berg, Bert; Eshuis, Jannes; Waterink, Wim; Nadolski, Rob; Berlanga, Adriana; Boers, Nanda; Koper, Rob

    2008-01-01

    Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R., Berlanga, A., Boers, N., & Koper, R. (2008). Effects of the ISIS Recommender System for Navigation Support in Self-Organized Learning Networks. In M. Kalz, R. Koper, V. Hornung-Prähauser & M. Luckmann (Eds.).

  20. Effects of the ISIS Recommender System for navigation support in self-organised Learning Networks

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Van den Berg, Bert; Eshuis, Jannes; Waterink, Wim; Nadolski, Rob; Berlanga, Adriana; Boers, Nanda; Koper, Rob

    2008-01-01

    Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Berlanga, A., Nadolski, R., Waterink, W., Boers, N., & Koper, R. (2008). Effects of the ISIS Recommender System for navigation support in self-organized Learning Networks. Presentation at the 4th conference Edumedia Conference 2008 Self-

  1. Effects of the ISIS Recommender System for navigation support in self-organised Learning Networks

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Van den Berg, Bert; Eshuis, Jannes; Waterink, Wim; Nadolski, Rob; Berlanga, Adriana; Boers, Nanda; Koper, Rob

    2009-01-01

    Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R. J., Berlanga, A. J., Boers, N., & Koper, R. (2009). Effects of the ISIS Recommender System for navigation support in self-organised Learning Networks. Journal of Educational Technology and Society, 12(3), 122-1

  2. Building a Firm Foundation: Recommendations for New York City's Job Training System. P/PV Briefs.

    Science.gov (United States)

    Buck, Maria L.

    This report describes the performance of New York City's Job Training Partnership Act (JTPA) adult training providers. It discusses challenges currently faced by providers, and recommends strategies for improving the performance of the city's employment and training system, including those arising from the implementation of the Workforce…

  3. Effects of the ISIS Recommender System for Navigation Support in Self-Organized Learning Networks

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Van den Berg, Bert; Eshuis, Jannes; Waterink, Wim; Nadolski, Rob; Berlanga, Adriana; Boers, Nanda; Koper, Rob

    2008-01-01

    Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R., Berlanga, A., Boers, N., & Koper, R. (2008). Effects of the ISIS Recommender System for Navigation Support in Self-Organized Learning Networks. In M. Kalz, R. Koper, V. Hornung-Prähauser & M. Luckmann (Eds.).

  4. TV Recommendation and Personalization Systems: Integrating Broadcast and Video On demand Services

    Directory of Open Access Journals (Sweden)

    SOARES, M.

    2014-02-01

    Full Text Available The expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.. To enable interoperability between different systems, programs? characteristics (title, genre, actors, etc. are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

  5. Implicit Learning as an Ability

    Science.gov (United States)

    Kaufman, Scott Barry; DeYoung, Caroline G.; Gray, Jeremy R.; Jimenez, Luis; Brown, Jamie; Mackintosh, Nicholas

    2010-01-01

    The ability to automatically and implicitly detect complex and noisy regularities in the environment is a fundamental aspect of human cognition. Despite considerable interest in implicit processes, few researchers have conceptualized implicit learning as an ability with meaningful individual differences. Instead, various researchers (e.g., Reber,…

  6. Adubarroz: a brazilian experience for fertilization and liming recommendation of irrigated rice via computational system

    Directory of Open Access Journals (Sweden)

    Felipe de Campos Carmona

    Full Text Available ABSTRACT: Recommendations for fertilizing irrigated rice in southern Brazil have been constantly evolving over years. In this process, the influence of factors such as the development cycle of varieties and sowing period increased. Thus, computational tools that take these and others important aspects into account can potentiate the fertilization response of rice. This study describes the computer program "ADUBARROZ". The software provides recommendations of fertilizer rates and liming requirements of irrigated rice, based on information entered by the user. The system takes various factors that regulate the crop response to fertilization into account. A final report is established with the graphical representation of input management over time.

  7. Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

    Science.gov (United States)

    Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

    2017-06-01

    This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

  8. Supporting Multi-Agent Reputation Calculation in the Wikipedia Recommender System

    DEFF Research Database (Denmark)

    Jensen, Christian D.

    2010-01-01

    the design of the system. The authors also provide a brief overview of the implementation of the WRS prototype. The WRS addresses the general problem of establishing trust in a collaboratively generated resource in a distributed multi-agent system, so the authors believe that the general architecture...... editorial policy that allows anybody, to create or modify articles. This has resulted in a broad and detailed coverage of subjects, but it has also caused problems relating to the quality of articles. The Wikipedia Recommender System (WRS) was developed to help human users determine the credibility...... articles that they have read. This makes the WRS a rating-based collaborative filtering system, which implements trust metrics to determine the weight of feedback from different recommenders. In this paper the authors describe the WRS outlining some of the requirements and constraints that shaped...

  9. Data You May Like: A Recommender System for Research Data Discovery

    Science.gov (United States)

    Devaraju, A.; Davy, R.; Hogan, D.

    2016-12-01

    Various data portals been developed to facilitate access to research datasets from different sources. For example, the Data Publisher for Earth & Environmental Science (PANGAEA), the Registry of Research Data Repositories (re3data.org), and the National Geoscience Data Centre (NGDC). Due to data quantity and heterogeneity, finding relevant datasets on these portals may be difficult and tedious. Keyword searches based on specific metadata elements or multi-key indexes may return irrelevant results. Faceted searches may be unsatisfactory and time consuming, especially when facet values are exhaustive. We need a much more intelligent way to complement existing searching mechanisms in order to enhance user experiences of the data portals. We developed a recommender system that helps users to find the most relevant research datasets on the CSIRO's Data Access Portal (DAP). The system is based on content-based filtering. We computed the similarity of datasets based on data attributes (e.g., descriptions, fields of research, location, contributors, and provenance) and inference from transaction logs (e.g., the relations among datasets and between queries and datasets). We improved the recommendation quality by assigning weights to data similarities. The weight values are drawn from a survey involving data users. The recommender results for a given dataset are accessible programmatically via a web service. Taking both data attributes and user actions into account, the recommender system will make it easier for researchers to find and reuse data offered through the data portal.

  10. Expert Meeting Report: Recommendations for Applying Water Heaters in Combination Space and Domestic Water Heating Systems

    Energy Technology Data Exchange (ETDEWEB)

    Rudd, A.; Ueno, K.; Bergey, D.; Osser, R.

    2012-07-01

    The topic of this meeting was 'Recommendations For Applying Water Heaters In Combination Space And Domestic Water Heating Systems.' Presentations and discussions centered on the design, performance, and maintenance of these combination systems, with the goal of developing foundational information toward the development of a Building America Measure Guideline on this topic. The meeting was held at the Westford Regency Hotel, in Westford, Massachusetts on 7/31/2011.

  11. Design and Implementation of ZigBee-Ontology-Based Exhibit Guidance and Recommendation System

    OpenAIRE

    Hung-Yu Chien; Shyr-Kuen Chen; Ching-Yang Lin; Jia-Ling Yan; Wei-Chen Liao; Huan-Yi Chu; Kuan-Ju Chen; Bo-Fan Lai; Yi-Ting Chen

    2013-01-01

    Even though information devices and systems have been widely applied in guidance service for museums and exhibitions, there are still several critical requirements unsatisfied. In our research, we systematically examine the requirements and then propose a new architecture for museum/exhibition guidance service; we further, based on ZigBee and ontology, implemented a new guide device and a new guidance and recommendation system. The contributions include (1) an extendable and comprehensive arc...

  12. The recommendation system knowledge representation and reasoning procedures under uncertainty for metal casting

    Directory of Open Access Journals (Sweden)

    S. Kluska-Nawarecka

    2015-01-01

    Full Text Available The paper presents an information system dedicated to requirements recommendation and knowledge sharing. It presents methodology of constructing domain knowledge base and application procedure on the example of production technology of Austempered Ductile Iron (ADI. For knowledge representation and reasoning Logic of Plausible Reasoning (LPR is used. Both equally applicable LPR for formalization the knowledge of foundry technology, as well as the described system solution have the unique character.

  13. USE: a multi-agent user-driven recommendation system for semantic knowledge extraction

    OpenAIRE

    Lopes, João Sousa; Álvarez Napagao, Sergio; Confalonieri, Roberto; Vázquez Salceda, Javier

    2009-01-01

    Semiotics is a field where research on Computer Science methodologies has focused, mainly concerning Syntax and Semantics. These methodologies, however, are lacking of some flexibility for the continuously evolving web community, in which the knowledge is classified with tags rather than with ontologies. In this paper we propose a multi-agent system for the recommendation of tagged pictures obtained from mainstream Web applications. The agents in this system execute a hybrid reasoning based o...

  14. Expert agreement on EULAR/EUSTAR recommendations for the management of systemic sclerosis.

    Science.gov (United States)

    Walker, Kyle M; Pope, Janet

    2011-07-01

    The European League Against Rheumatism/EULAR Scleroderma Trials and Research group (EULAR/EUSTAR) has published recommendations for the management of systemic sclerosis (SSc). Members of the Scleroderma Clinical Trials Consortium and the Canadian Scleroderma Research Group were surveyed regarding their level of agreement with the recommendations. A survey containing the 14 EULAR/EUSTAR recommendations asked participants to indicate their level of agreement with each on a 10-point scale, from 0 (not at all) to 9 (completely agree). The survey was sent to 117 people, and 66 replies were received (56% response rate). Exceptions to generally high agreement included the use of iloprost and bosentan for digital vasculopathy, methotrexate for skin involvement, and bosentan and epoprostenol for pulmonary arterial hypertension (PAH; all EULAR/EUSTAR recommendation authors shared a similar level of agreement compared to those who were not, except for the use of proton pump inhibitors for the prevention of SSc-related gastroesophageal reflux disease, esophageal ulcers, and strictures. EULAR/EUSTAR recommendations were relatively well accepted among SSc experts. Overall reduced agreement may be due to the modest efficacy of some agents (such as methotrexate for the skin). Some regional disagreement is likely because of access differences.

  15. Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects

    Directory of Open Access Journals (Sweden)

    Ricardo AZAMBUJA SILVEIRA

    2016-07-01

    Full Text Available This paper discusses some important issues regarding the the management of Learning objects covering searching over repositories and different approaches of recommendation systems and presents a multiagent system based application model for indexing, retrieving and recommending learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata (data about data standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the relevance of the results we propose an information retrieval model based on a multiagent system approach and an ontological model to describe the covered knowledge domain.

  16. Securing recommender systems against shilling attacks using social-based clustering

    KAUST Repository

    Zhang, Xiangliang

    2013-07-01

    Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com. © 2013 Springer Science+Business Media New York & Science Press, China.

  17. Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

    Institute of Scientific and Technical Information of China (English)

    Xiang-Liang Zhang; Tak Man Desmond Lee; Georgios Pitsilis

    2013-01-01

    Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services.Despite their great adoption in the user communities,RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems.With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks.In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks.We propose two algorithms,CLUTR and WCLUTR,to combine clustering with "trust" among users.We demonstrate that CLUTR and WCLUTR enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com.

  18. Feasibility of Integrated Menu Recommendation and Self-Order System for Small-Scale Restaurants

    Science.gov (United States)

    Kashima, Tomoko; Matsumoto, Shimpei; Ishii, Hiroaki

    2010-10-01

    In recent years, point of sales (POS) systems with order function have been developed for restaurants. Since expensive apparatus and system are required for installing POS systems, usually only large-scale restaurant chains can afford to introduce them. In this research, we consider the POS management in a restaurant, which cooperates with an automatic order function by using a personal digital device aiming at the safety of the food, pursuit of service, and further operational efficiency improvements, such as foods management, accounting treatment, and ordering work. In traditional POS systems, information recommendation technology is not taken into consideration. We realize the recommendation of a menu according to the user's preference using rough sets and menu planning based on stock status by applying information recommendation technology. Therefore, we believe that this system can be used in comfort with regard to freshness of foods, allergy, diabetes, etc. Furthermore, due to the reduction of the personnel expenses by an operational efficiency improvement such technology becomes even feasible for small-scale stores.

  19. The recommender system for virtual items in MMORPGs based on a novel collaborative filtering approach

    Science.gov (United States)

    Li, S. G.; Shi, L.

    2014-10-01

    The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers' satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers' characteristics and the similarity of customers' satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer.

  20. A Real-Time Taxicab Recommendation System Using Big Trajectories Data

    Directory of Open Access Journals (Sweden)

    Pengpeng Chen

    2017-01-01

    Full Text Available Carpooling is becoming a more and more significant traffic choice, because it can provide additional service options, ease traffic congestion, and reduce total vehicle exhaust emissions. Although some recommendation systems have proposed taxicab carpooling services recently, they cannot fully utilize and understand the known information and essence of carpooling. This study proposes a novel recommendation algorithm, which provides either a vacant or an occupied taxicab in response to a passenger’s request, called VOT. VOT recommends the closest vacant taxicab to passengers. Otherwise, VOT infers destinations of occupied taxicabs by similarity comparison and clustering algorithms and then recommends the occupied taxicab heading to a close destination to passengers. Using an efficient large data-processing framework, Spark, we greatly improve the efficiency of large data processing. This study evaluates VOT with a real-world dataset that contains 14747 taxicabs’ GPS data. Results show that the ratio of range (between forecasted and actual destinations of less than 900 M can reach 90.29%. The total mileage to deliver all passengers is significantly reduced (47.84% on average. Specifically, the reduced total mileage of nonrush hours outperforms other systems by 35%. VOT and others have similar performances in actual detour ratio, even better in rush hours.

  1. TogoDoc server/client system: smart recommendation and efficient management of life science literature.

    Science.gov (United States)

    Iwasaki, Wataru; Yamamoto, Yasunori; Takagi, Toshihisa

    2010-12-13

    In this paper, we describe a server/client literature management system specialized for the life science domain, the TogoDoc system (Togo, pronounced Toe-Go, is a romanization of a Japanese word for integration). The server and the client program cooperate closely over the Internet to provide life scientists with an effective literature recommendation service and efficient literature management. The content-based and personalized literature recommendation helps researchers to isolate interesting papers from the "tsunami" of literature, in which, on average, more than one biomedical paper is added to MEDLINE every minute. Because researchers these days need to cover updates of much wider topics to generate hypotheses using massive datasets obtained from public databases or omics experiments, the importance of having an effective literature recommendation service is rising. The automatic recommendation is based on the content of personal literature libraries of electronic PDF papers. The client program automatically analyzes these files, which are sometimes deeply buried in storage disks of researchers' personal computers. Just saving PDF papers to the designated folders makes the client program automatically analyze and retrieve metadata, rename file names, synchronize the data to the server, and receive the recommendation lists of newly published papers, thus accomplishing effortless literature management. In addition, the tag suggestion and associative search functions are provided for easy classification of and access to past papers (researchers who read many papers sometimes only vaguely remember or completely forget what they read in the past). The TogoDoc system is available for both Windows and Mac OS X and is free. The TogoDoc Client software is available at http://tdc.cb.k.u-tokyo.ac.jp/, and the TogoDoc server is available at https://docman.dbcls.jp/pubmed_recom.

  2. TogoDoc server/client system: smart recommendation and efficient management of life science literature.

    Directory of Open Access Journals (Sweden)

    Wataru Iwasaki

    Full Text Available In this paper, we describe a server/client literature management system specialized for the life science domain, the TogoDoc system (Togo, pronounced Toe-Go, is a romanization of a Japanese word for integration. The server and the client program cooperate closely over the Internet to provide life scientists with an effective literature recommendation service and efficient literature management. The content-based and personalized literature recommendation helps researchers to isolate interesting papers from the "tsunami" of literature, in which, on average, more than one biomedical paper is added to MEDLINE every minute. Because researchers these days need to cover updates of much wider topics to generate hypotheses using massive datasets obtained from public databases or omics experiments, the importance of having an effective literature recommendation service is rising. The automatic recommendation is based on the content of personal literature libraries of electronic PDF papers. The client program automatically analyzes these files, which are sometimes deeply buried in storage disks of researchers' personal computers. Just saving PDF papers to the designated folders makes the client program automatically analyze and retrieve metadata, rename file names, synchronize the data to the server, and receive the recommendation lists of newly published papers, thus accomplishing effortless literature management. In addition, the tag suggestion and associative search functions are provided for easy classification of and access to past papers (researchers who read many papers sometimes only vaguely remember or completely forget what they read in the past. The TogoDoc system is available for both Windows and Mac OS X and is free. The TogoDoc Client software is available at http://tdc.cb.k.u-tokyo.ac.jp/, and the TogoDoc server is available at https://docman.dbcls.jp/pubmed_recom.

  3. Chinese implicit leadership theory.

    Science.gov (United States)

    Ling, W; Chia, R C; Fang, L

    2000-12-01

    In a 1st attempt to identify an implicit theory of leadership among Chinese people, the authors developed the Chinese Implicit Leadership Scale (CILS) in Study 1. In Study 2, they administered the CILS to 622 Chinese participants from 5 occupation groups, to explore differences in perceptions of leadership. Factor analysis yielded 4 factors of leadership: Personal Morality, Goal Efficiency, Interpersonal Competence, and Versatility. Social groups differing in age, gender, education level, and occupation rated these factors. Results showed no significant gender differences, and the underlying cause for social group differences was education level. All groups gave the highest ratings to Interpersonal Competence, reflecting the enormous importance of this factor, which is consistent with Chinese collectivist values.

  4. A study and analysis of recommendation systems for location-based social network (LBSN with big data

    Directory of Open Access Journals (Sweden)

    Murale Narayanan

    2016-03-01

    Full Text Available Recommender systems play an important role in our day-to-day life. A recommender system automatically suggests an item to a user that he/she might be interested in. Small-scale datasets are used to provide recommendations based on location, but in real time, the volume of data is large. We have selected Foursquare dataset to study the need for big data in recommendation systems for location-based social network (LBSN. A few quality parameters like parallel processing and multimodal interface have been selected to study the need for big data in recommender systems. This paper provides a study and analysis of quality parameters of recommendation systems for LBSN with big data.

  5. Implicit Real Vector Automata

    Directory of Open Access Journals (Sweden)

    Jean-François Degbomont

    2010-10-01

    Full Text Available This paper addresses the symbolic representation of non-convex real polyhedra, i.e., sets of real vectors satisfying arbitrary Boolean combinations of linear constraints. We develop an original data structure for representing such sets, based on an implicit and concise encoding of a known structure, the Real Vector Automaton. The resulting formalism provides a canonical representation of polyhedra, is closed under Boolean operators, and admits an efficient decision procedure for testing the membership of a vector.

  6. Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems

    Science.gov (United States)

    Parra-Arnau, Javier; Rebollo-Monedero, David; Forné, Jordi

    2014-03-01

    Recommendation systems are information-filtering systems that tailor information to users on the basis of knowledge about their preferences. The ability of these systems to profile users is what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. In this paper we investigate a privacy-enhancing technology that aims at hindering an attacker in its efforts to accurately profile users based on the items they rate. Our approach capitalizes on the combination of two perturbative mechanisms---the forgery and the suppression of ratings. While this technique enhances user privacy to a certain extent, it inevitably comes at the cost of a loss in data utility, namely a degradation of the recommendation's accuracy. In short, it poses a trade-off between privacy and utility. The theoretical analysis of said trade-off is the object of this work. We measure privacy as the Kullback-Leibler divergence between the user's and the population's item distributions, and quantify utility as the proportion of ratings users consent to forge and eliminate. Equipped with these quantitative measures, we find a closed-form solution to the problem of optimal forgery and suppression of ratings, and characterize the trade-off among privacy, forgery rate and suppression rate. Experimental results on a popular recommendation system show how our approach may contribute to privacy enhancement.

  7. Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems.

    Science.gov (United States)

    Wu, Jun; Su, Zhou; Wang, Shen; Li, Jianhua

    2017-07-30

    Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on "friend" relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems.

  8. Strategies of detecting Profile-injection attacks in E-Commerce Recommender System: A survey Partha

    Directory of Open Access Journals (Sweden)

    Sarathi Chakraborty,

    2015-12-01

    Full Text Available E-commerce recommender systems are vulnerable to different types of shilling attack where the attacker influences the recommendation procedure in favor of him by inserting fake user-profiles into the system. From one point of view, the attacks can be of type push or nuke-either to promote or to demote a product. On the other hand, attacks can be classified as high-knowledge or low-knowledge attack depending on the amount of system knowledge required for making the attack successful. Several research works have been done in the last two decades for defending attacks on recommender systems. In this paper, we have surveyed the major works done in this area by different researchers. After a brief explanation of different attack types and attack models, we discussed the attack detection strategies proposed by the researchers mainly under five categories- Generic and model specific attribute based, rating distribution based, outlier analysis based, statistical approach based and clustering based.

  9. A fully-implicit model of the global ocean circulation

    NARCIS (Netherlands)

    Weijer, Wilbert; Dijkstra, Henk A.; Öksüzoğlu, Hakan; Wubs, Fred W.; Niet, Arie C. de

    2003-01-01

    With the recent developments in the solution methods for large-dimensional nonlinear algebraic systems, fully-implicit ocean circulation models are now becoming feasible. In this paper, the formulation of such a three-dimensional global ocean model is presented. With this implicit model, the

  10. The Existence of Implicit Racial Bias in Nursing Faculty

    Science.gov (United States)

    Fitzsimmons, Kathleen A.

    2009-01-01

    This study examined the existence of implicit racial bias in nursing faculty using the Implicit Association Test (IAT). It was conducted within a critical race theory framework where race was seen as a permanent, pervasive, and systemic condition, not an individual process. The study was fueled by data showing continued disparate academic and…

  11. Automatic stress-relieving music recommendation system based on photoplethysmography-derived heart rate variability analysis.

    Science.gov (United States)

    Shin, Il-Hyung; Cha, Jaepyeong; Cheon, Gyeong Woo; Lee, Choonghee; Lee, Seung Yup; Yoon, Hyung-Jin; Kim, Hee Chan

    2014-01-01

    This paper presents an automatic stress-relieving music recommendation system (ASMRS) for individual music listeners. The ASMRS uses a portable, wireless photoplethysmography module with a finger-type sensor, and a program that translates heartbeat signals from the sensor to the stress index. The sympathovagal balance index (SVI) was calculated from heart rate variability to assess the user's stress levels while listening to music. Twenty-two healthy volunteers participated in the experiment. The results have shown that the participants' SVI values are highly correlated with their prespecified music preferences. The sensitivity and specificity of the favorable music classification also improved as the number of music repetitions increased to 20 times. Based on the SVI values, the system automatically recommends favorable music lists to relieve stress for individuals.

  12. Implicit Memory in Music and Language

    Directory of Open Access Journals (Sweden)

    Marc eEttlinger

    2011-09-01

    Full Text Available Research on music and language in recent decades has focused on their overlapping neurophysiological, perceptual, and cognitive underpinnings, ranging from the mechanism for encoding basic auditory cues to the mechanism for detecting violations in phrase structure. These overlaps have most often been identified in musicians with musical knowledge that was acquired explicitly, through formal training. In this paper, we review independent bodies of work in music and language that suggest an important role for implicitly acquired knowledge, implicit memory, and their associated neural structures in the acquisition of linguistic or musical grammar. These findings motivate potential new work that examines music and language comparatively in the context of the implicit memory system.

  13. Geographic information system for improving maternal and newborn health: recommendations for policy and programs.

    Science.gov (United States)

    Molla, Yordanos B; Rawlins, Barbara; Makanga, Prestige Tatenda; Cunningham, Marc; Ávila, Juan Eugenio Hernández; Ruktanonchai, Corrine Warren; Singh, Kavita; Alford, Sylvia; Thompson, Mira; Dwivedi, Vikas; Moran, Allisyn C; Matthews, Zoe

    2017-01-11

    This correspondence argues and offers recommendations for how Geographic Information System (GIS) applied to maternal and newborn health data could potentially be used as part of the broader efforts for ending preventable maternal and newborn mortality. These recommendations were generated from a technical consultation on reporting and mapping maternal deaths that was held in Washington, DC from January 12 to 13, 2015 and hosted by the United States Agency for International Development's (USAID) global Maternal and Child Survival Program (MCSP). Approximately 72 participants from over 25 global health organizations, government agencies, donors, universities, and other groups participated in the meeting.The meeting placed emphases on how improved use of mapping could contribute to the post-2015 United Nation's Sustainable Development Goals (SDGs), agenda in general and to contribute to better maternal and neonatal health outcomes in particular. Researchers and policy makers have been calling for more equitable improvement in Maternal and Newborn Health (MNH), specifically addressing hard-to-reach populations at sub-national levels. Data visualization using mapping and geospatial analyses play a significant role in addressing the emerging need for improved spatial investigation at subnational scale. This correspondence identifies key challenges and recommendations so GIS may be better applied to maternal health programs in resource poor settings. The challenges and recommendations are broadly grouped into three categories: ancillary geospatial and MNH data sources, technical and human resources needs and community participation.

  14. The Military Spouse Education and Career Opportunities Program: Recommendations for an Internal Monitoring System

    Science.gov (United States)

    2016-01-01

    the word about available benefits and services of SECO and the importance of portable career choices for spouses of career military personnel...eligible indicated that they did not use a My Career Advancement Account Scholarship in the previous year because family or personal obligations...The Military Spouse Education and Career Opportunities Program Recommendations for an Internal Monitoring System Gabriella C. Gonzalez, Laura L

  15. Recommendations for Evaluating Multiple Filters in Ballast Water Management Systems for US Type Approval

    Science.gov (United States)

    2016-01-01

    water options are summarized in Table 5. Additionally, NIOZ offers BWMS vendors the option to experiment using the NIOZ facility prior to setting up... supply volume and pressure requirements Fresh water requirements Volume and flow requirements for fresh water supply Electrical supply requirements...Recommendations for Evaluating Multiple Filters in Ballast Water Management Systems for US Type Approval Lisa A. Drake1, Timothy P. Wier2, Evan

  16. Smartparticipation a fuzzy-based recommender system for political community-building

    CERN Document Server

    Terán Tamayo, Luis Fernando

    2014-01-01

    In this book a fuzzy-based recommender system architecture for stimulating political participation and collaboration is proposed. It showcases the ""Smart Participation"" project, which uses the database of ""smart vote"", a well-known voting advice application (VAA) for local, cantonal and national elections in Switzerland. Additionally, an evaluation framework for e Participation is presented, which allows to analyze different projects and their development towards the enhancement of citizen's participation and empowerment. The book demonstrates the potential for building political communiti

  17. New Artificial Immune System Approach Based on Monoclonal Principle for Job Recommendation

    OpenAIRE

    Shaha Al-Otaibi; Mourad Ykhlef

    2016-01-01

    Finding the best solution for an optimization problem is a tedious task, specifically in the presence of enormously represented features. When we handle a problem such as job recommendations that have a diversity of their features, we should rely to metaheuristics. For example, the Artificial Immune System which is a novel computational intelligence paradigm achieving diversification and exploration of the search space as well as exploitation of the good solutions were reached in reasonable t...

  18. A random map implementation of implicit filters

    CERN Document Server

    Morzfeld, Matthias; Atkins, Ethan; Chorin, Alexandre J

    2011-01-01

    Implicit particle filters for data assimilation generate high-probability samples by representing each particle location as a separate function of a common reference variable. This representation requires that a certain underdetermined equation be solved for each particle and at each time an observation becomes available. We present a new implementation of implicit filters in which we find the solution of the equation via a random map. As examples, we assimilate data for a stochastically driven Lorenz system with sparse observations and for a stochastic Kuramoto-Sivashinski equation with observations that are sparse in both space and time.

  19. New Artificial Immune System Approach Based on Monoclonal Principle for Job Recommendation

    Directory of Open Access Journals (Sweden)

    Shaha Al-Otaibi

    2016-04-01

    Full Text Available Finding the best solution for an optimization problem is a tedious task, specifically in the presence of enormously represented features. When we handle a problem such as job recommendations that have a diversity of their features, we should rely to metaheuristics. For example, the Artificial Immune System which is a novel computational intelligence paradigm achieving diversification and exploration of the search space as well as exploitation of the good solutions were reached in reasonable time. Unfortunately, in problems with diversity nature such job recommendation, it produces a huge number of antibodies that causes a large number of matching processes affect the system efficiency. To leverage this issue, we present a new intelligence algorithm inspired by immunology based on monoclonal antibodies production principle that, up to our knowledge, has never applied in science and engineering problems. The proposed algorithm recommends ranked list of best applicants for a certain job. We discussed the design issues, as well as the immune system processes that should be applied to the problem. Finally, the experiments are conducted that shown an excellence of our approach.

  20. Implicit learning as an ability.

    Science.gov (United States)

    Kaufman, Scott Barry; Deyoung, Colin G; Gray, Jeremy R; Jiménez, Luis; Brown, Jamie; Mackintosh, Nicholas

    2010-09-01

    The ability to automatically and implicitly detect complex and noisy regularities in the environment is a fundamental aspect of human cognition. Despite considerable interest in implicit processes, few researchers have conceptualized implicit learning as an ability with meaningful individual differences. Instead, various researchers (e.g., Reber, 1993; Stanovich, 2009) have suggested that individual differences in implicit learning are minimal relative to individual differences in explicit learning. In the current study of English 16-17year old students, we investigated the association of individual differences in implicit learning with a variety of cognitive and personality variables. Consistent with prior research and theorizing, implicit learning, as measured by a probabilistic sequence learning task, was more weakly related to psychometric intelligence than was explicit associative learning, and was unrelated to working memory. Structural equation modeling revealed that implicit learning was independently related to two components of psychometric intelligence: verbal analogical reasoning and processing speed. Implicit learning was also independently related to academic performance on two foreign language exams (French, German). Further, implicit learning was significantly associated with aspects of self-reported personality, including intuition, Openness to Experience, and impulsivity. We discuss the implications of implicit learning as an ability for dual-process theories of cognition, intelligence, personality, skill learning, complex cognition, and language acquisition.

  1. The proposed architecture of the Internet of Things based recommender systems for intelligent building in Tehran

    Directory of Open Access Journals (Sweden)

    Maryam Haji Shah Karam

    2016-12-01

    Full Text Available Today, the need in many cities are complex and therefore require smart cities. The complexity on the one hand, mainly because a lot of communication between various systems such as transport, communication networks, business systems, and on the other hand, citizens who are in contact with all of these systems, is . The synchronization process fast cities with innovative technology, quickly and efficiently, in turn, has a significant impact on the complexity. In this regard, one of the most important requirements for smart city planning, efficient use of information technology and communication. So to implement a Smart City, the need for clear and precise definition of it. Smart city concepts to better understand the implementation and evaluation of such domains involved better "infrastructure environment" and "environmental services" is. Much research has been done in relation to smart cities, but none on recommender systems and crowdsourcing, are not specific to the architecture. This research, conducted in Tehran smart. Then, after analyzing the different architectures based on the results of the research literature, architecture is proposed. In this architecture, the five-layer infrastructure, data collection, management and processing of data, services and applications are anticipated. The components of each layer are explained in detail. Finally, the study concluded that innovation in traditional architecture by taking advantage of the idea of ​​"crowdsourcing" and "recommender systems" can be improved in intelligent transportation systems, intelligent energy management systems smart Home smart city was in the area.

  2. The Skills, Competences, and Attitude toward Information and Communications Technology Recommender System: an online support program for teachers with personalized recommendations

    Science.gov (United States)

    Revilla Muñoz, Olga; Alpiste Penalba, Francisco; Fernández Sánchez, Joaquín

    2016-01-01

    Teachers deal with Information and Communications Technology (ICT) every day and they often have to solve problems by themselves. To help them in coping with this issue, an online support program has been created, where teachers can pose their problems on ICT and they can receive solutions from other teachers. A Recommender System has been defined and implemented into the support program to suggest to each teacher the most suitable solution based on her Skills, Competences, and Attitude toward ICT (SCAT-ICT). The support program has initially been populated with 70 problems from 86 teachers. 30 teachers grouped these problems into six categories with the card-sorting technique. Real solutions to these problems have been proposed by 25 trained teachers. Finally, 17 teachers evaluated the usability of the support program and the Recommender System, where results showed a high score on the standardized System Usability Scale.

  3. Report on RecSys 2015 Workshop on New Trends in Content-Based Recommender Systems (CBRecSys 2015)

    DEFF Research Database (Denmark)

    Bogers, Toine; Koolen, Marijn

    2016-01-01

    This article reports on the CBRecSys 2015 workshop, the second edition of the workshop on new trends in content-based recommender systems, co-located with RecSys 2015 in Vienna, Austria. Content-based recommendation has been applied successfully in many different domains, but it has not seen the ...... venue for work dedicated to all aspects of content-based recommender systems....

  4. CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Diffusion-Based Recommendation in Collaborative Tagging Systems

    Science.gov (United States)

    Shang, Ming-Sheng; Zhang, Zi-Ke

    2009-11-01

    Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and therefore have the potential to help in improving better personalized recommendations. We propose a diffusion-based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset: Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.

  5. Utilizing Implicit User Feedback to Improve Interactive Video Retrieval

    Directory of Open Access Journals (Sweden)

    Stefanos Vrochidis

    2011-01-01

    Full Text Available This paper describes an approach to exploit the implicit user feedback gathered during interactive video retrieval tasks. We propose a framework, where the video is first indexed according to temporal, textual, and visual features and then implicit user feedback analysis is realized using a graph-based methodology. The generated graph encodes the semantic relations between video segments based on past user interaction and is subsequently used to generate recommendations. Moreover, we combine the visual features and implicit feedback information by training a support vector machine classifier with examples generated from the aforementioned graph in order to optimize the query by visual example search. The proposed framework is evaluated by conducting real-user experiments. The results demonstrate that significant improvement in terms of precision and recall is reported after the exploitation of implicit user feedback, while an improved ranking is presented in most of the evaluated queries by visual example.

  6. Implicit Hamiltonian formulation of bond graphs

    NARCIS (Netherlands)

    Golo, G.; Schaft, A.J. van der; Breedveld, P.C.; Maschke, B.M.

    2003-01-01

    This paper deals with mathematical formulation of bond graphs. It is proven that the power continuous part of bond graphs, the junction structure, can be associated with a Dirac structure and that equations describing a bond graph model correspond to an implicit port-controlled Hamiltonian system wi

  7. EULAR evidence‐based recommendations on the management of systemic glucocorticoid therapy in rheumatic diseases

    Science.gov (United States)

    Hoes, J N; Jacobs, J W G; Boers, M; Boumpas, D; Buttgereit, F; Caeyers, N; Choy, E H; Cutolo, M; Da Silva, J A P; Esselens, G; Guillevin, L; Hafstrom, I; Kirwan, J R; Rovensky, J; Russell, A; Saag, K G; Svensson, B; Westhovens, R; Zeidler, H; Bijlsma, J W J

    2007-01-01

    Objective To develop evidence‐based recommendations for the management of systemic glucocorticoid (GC) therapy in rheumatic diseases. Methods The multidisciplinary guideline development group from 11 European countries, Canada and the USA consisted of 15 rheumatologists, 1 internist, 1 rheumatologist–epidemiologist, 1 health professional, 1 patient and 1 research fellow. The Delphi method was used to agree on 10 key propositions related to the safe use of GCs. A systematic literature search of PUBMED, EMBASE, CINAHL, and Cochrane Library was then used to identify the best available research evidence to support each of the 10 propositions. The strength of recommendation was given according to research evidence, clinical expertise and perceived patient preference. Results The 10 propositions were generated through three Delphi rounds and included patient education, risk factors, adverse effects, concomitant therapy (ie, non‐steroidal anti‐inflammatory drugs, gastroprotection and cyclo‐oxygenase‐2 selective inhibitors, calcium and vitamin D, bisphosphonates) and special safety advice (ie, adrenal insufficiency, pregnancy, growth impairment). Conclusion Ten key recommendations for the management of systemic GC‐therapy were formulated using a combination of systematically retrieved research evidence and expert consensus. There are areas of importance that have little evidence (ie, dosing and tapering strategies, timing, risk factors and monitoring for adverse effects, perioperative GC‐replacement) and need further research; therefore also a research agenda was composed. PMID:17660219

  8. Diffusion-based recommendation with trust relations on tripartite graphs

    Science.gov (United States)

    Wang, Ximeng; Liu, Yun; Zhang, Guangquan; Xiong, Fei; Lu, Jie

    2017-08-01

    The diffusion-based recommendation approach is a vital branch in recommender systems, which successfully applies physical dynamics to make recommendations for users on bipartite or tripartite graphs. Trust links indicate users’ social relations and can provide the benefit of reducing data sparsity. However, traditional diffusion-based algorithms only consider rating links when making recommendations. In this paper, the complementarity of users’ implicit and explicit trust is exploited, and a novel resource-allocation strategy is proposed, which integrates these two kinds of trust relations on tripartite graphs. Through empirical studies on three benchmark datasets, our proposed method obtains better performance than most of the benchmark algorithms in terms of accuracy, diversity and novelty. According to the experimental results, our method is an effective and reasonable way to integrate additional features into the diffusion-based recommendation approach.

  9. Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications.

    Directory of Open Access Journals (Sweden)

    Titipat Achakulvisut

    Full Text Available Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.

  10. Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications.

    Science.gov (United States)

    Achakulvisut, Titipat; Acuna, Daniel E; Ruangrong, Tulakan; Kording, Konrad

    2016-01-01

    Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.

  11. Emergence of scale-free leadership structure in social recommender systems

    CERN Document Server

    Zhou, Tao; Cimini, Giulio; Zhang, Zi-Ke; Zhang, Yi-Cheng

    2011-01-01

    The study of the organization of social networks is important for understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems.

  12. Clustering Algorithms in Hybrid Recommender System on MovieLens Data

    Directory of Open Access Journals (Sweden)

    Kuzelewska Urszula

    2014-08-01

    Full Text Available Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files.

  13. Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning

    DEFF Research Database (Denmark)

    Drachsler, Hendrik; Bogers, Toine; Vuorikari, Riina

    2010-01-01

    This paper raises the issue of missing standardised data sets for recommender systems in Technology Enhanced Learning (TEL) that can be used as benchmarks to compare different recommendation approaches. It discusses how suitable data sets could be created according to some initial suggestions......, and investigates a number of steps that may be followed in order to develop reference data sets that will be adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing of data sets by taking into account legal protection rights. Finally, an initial...... elaboration of a representation and exchange format for sharable TEL data sets is carried out. The paper concludes with future research needs....

  14. PipeCF:a DHT-based Collaborative Filtering recommendation system

    Institute of Scientific and Technical Information of China (English)

    SHEN Rui-min; YANG Fan; HAN Peng; XIE Bo

    2005-01-01

    Collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalability as calculation complexity increases rapidly both in time and space when the record in the user database increases. Peer-to-peer (P2P) network has attracted much attention because of its advantage of scalability as an alternative architecture for CF systems. In this paper, authors propose a decentralized CF algorithm, called PipeCF, based on distributed hash table (DHT) method which is the most popular P2P routing algorithm because of its efficiency, scalability, and robustness. Authors also propose two novel approaches: significance refinement (SR) and unanimous amplification (UA), to improve the scalability and prediction accuracy of DHT-based CF algorithm. The experimental data show that our DHT-based CF system has better prediction accuracy, efficiency and scalability than traditional CF systems.

  15. Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.

    Science.gov (United States)

    Sadasivam, Rajani Shankar; Cutrona, Sarah L; Kinney, Rebecca L; Marlin, Benjamin M; Mazor, Kathleen M; Lemon, Stephenie C; Houston, Thomas K

    2016-03-07

    What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.

  16. Healthcare System Distrust, Physician Trust, and Patient Discordance with Adjuvant Breast Cancer Treatment Recommendations.

    Science.gov (United States)

    Dean, Lorraine T; Moss, Shadiya L; McCarthy, Anne Marie; Armstrong, Katrina

    2017-09-29

    Adjuvant therapy after breast cancer surgery decreases recurrence and increases survival, yet not all women receive and complete it. Previous research has suggested that distrust in medical institutions plays a role in who initiates adjuvant treatment, but has not assessed treatment completion treatment, nor the potential mediating role of physician distrust. Women listed in Pennsylvania and Florida cancer registries, who were under the age of 65 when diagnosed with localized invasive breast cancer between 2005 and 2007, were surveyed by mail in 2007-2009. Survey participants self-reported: demographics; cancer stage and treatments; treatment discordance, as defined by not following their surgeon or oncologist treatment recommendation; healthcare system distrust, and physician trust. Age and cancer stage were verified against cancer registry records. Logistic regression assessed the relationship between highest and lowest tertiles of healthcare system distrust and the dichotomous outcome of treatment discordance, controlling for demographics and clinical treatment factors, and testing for mediation by physician trust. Of the 2,754 participants, 30.2% (n=832) reported not pursing at least one recommended treatment. The mean age was 52. Patients in the highest tertile of healthcare system distrust were 22% more likely to report treatment discordance than the lowest tertile; physician trust did not mediate the association between healthcare system distrust and treatment discordance. Healthcare system distrust is positively associated with treatment discordance, defined as failure to initiate or complete physician recommended adjuvant treatment after breast cancer. Interventions should test whether or not resolving institutional distrust reduces treatment discordance. Copyright ©2017, American Association for Cancer Research.

  17. Information Filtering via Implicit Trust-based Network

    CERN Document Server

    Xuan, Zhao-Guo; Liu, Jian-Guo

    2011-01-01

    Based on the user-item bipartite network, collaborative filtering (CF) recommender systems predict users' interests according to their history collections, which is a promising way to solve the information exploration problem. However, CF algorithm encounters cold start and sparsity problems. The trust-based CF algorithm is implemented by collecting the users' trust statements, which is time-consuming and must use users' private friendship information. In this paper, we present a novel measurement to calculate users' implicit trust-based correlation by taking into account their average ratings, rating ranges, and the number of common rated items. By applying the similar idea to the items, a item-based CF algorithm is constructed. The simulation results on three benchmark data sets show that the performances of both user-based and item-based algorithms could be enhanced greatly. Finally, a hybrid algorithm is constructed by integrating the user-based and item-based algorithms, the simulation results indicate t...

  18. Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2014)

    DEFF Research Database (Denmark)

    Bogers, Toine; Koolen, Marijn; Cantádor, Ivan

    2014-01-01

    in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how...

  19. Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)

    DEFF Research Database (Denmark)

    Bogers, Toine; Koolen, Marijn

    2015-01-01

    in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how...

  20. Constructing a web recommender system using web usage mining and user’s profiles

    Directory of Open Access Journals (Sweden)

    T. Mombeini

    2014-12-01

    Full Text Available The World Wide Web is a great source of information, which is nowadays being widely used due to the availability of useful information changing, dynamically. However, the large number of webpages often confuses many users and it is hard for them to find information on their interests. Therefore, it is necessary to provide a system capable of guiding users towards their desired choices and services. Recommender systems search among a large collection of user interests and recommend those, which are likely to be favored the most by the user. Web usage mining was designed to function on web server records, which are included in user search results. Therefore, recommender servers use the web usage mining technique to predict users’ browsing patterns and recommend those patterns in the form of a suggestion list. In this article, a recommender system based on web usage mining phases (online and offline was proposed. In the offline phase, the first step is to analyze user access records to identify user sessions. Next, user profiles are built using data from server records based on the frequency of access to pages, the time spent by the user on each page and the date of page view. Date is of importance since it is more possible for users to request new pages more than old ones and old pages are less probable to be viewed, as users mostly look for new information. Following the creation of user profiles, users are categorized in clusters using the Fuzzy C-means clustering algorithm and S(c criterion based on their similarities. In the online phase, a neural network is offered to identify the suggested model while online suggestions are generated using the suggestion module for the active user. Search engines analyze suggestion lists based on rate of user interest in pages and page rank and finally suggest appropriate pages to the active user. Experiments show that the proposed method of predicting user recent requested pages has more accuracy and

  1. The Universal Recommender

    CERN Document Server

    Kunegis, Jérôme; Umbrath, Winfried

    2009-01-01

    We describe the Universal Recommender, a recommender system for semantic datasets that generalizes domain-specific recommenders such a content-based, collaborative, social, bibliographic, lexicographic, hybrid and other recommenders. In contrast to existing recommender systems, the Universal Recommender applies to any dataset that allows a semantic representation. We describe the scalable three-stage architecture of the Universal Recommender and its application to Internet Protocol Television (IPTV). To achieve good recommendation accuracy, several novel machine learning and optimization problems are identified. We finally give a brief argument supporting the need for machine learning recommenders.

  2. Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.

    Science.gov (United States)

    Yin, Yuyu; Yu, Fangzheng; Xu, Yueshen; Yu, Lifeng; Mu, Jinglong

    2017-09-08

    Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.

  3. Implicitly positive about alcohol? Implicit positive associations predict drinking behavior

    NARCIS (Netherlands)

    Houben, K.; Wiers, R.W.H.J.

    2008-01-01

    Research using unipolar Implicit Association Tests (IATs) demonstrated that positive but not negative implicit alcohol associations are related to drinking behavior. However, the relative nature of the IAT with respect to target concepts (i.e., alcohol vs. soft drinks) obscures the interpretation of

  4. Implicitly positive about alcohol? Implicit positive associations predict drinking behavior

    NARCIS (Netherlands)

    Houben, K.; Wiers, R.W.H.J.

    2008-01-01

    Research using unipolar Implicit Association Tests (IATs) demonstrated that positive but not negative implicit alcohol associations are related to drinking behavior. However, the relative nature of the IAT with respect to target concepts (i.e., alcohol vs. soft drinks) obscures the interpretation of

  5. Adapting implicit methods to parallel processors

    Energy Technology Data Exchange (ETDEWEB)

    Reeves, L.; McMillin, B.; Okunbor, D.; Riggins, D. [Univ. of Missouri, Rolla, MO (United States)

    1994-12-31

    When numerically solving many types of partial differential equations, it is advantageous to use implicit methods because of their better stability and more flexible parameter choice, (e.g. larger time steps). However, since implicit methods usually require simultaneous knowledge of the entire computational domain, these methods axe difficult to implement directly on distributed memory parallel processors. This leads to infrequent use of implicit methods on parallel/distributed systems. The usual implementation of implicit methods is inefficient due to the nature of parallel systems where it is common to take the computational domain and distribute the grid points over the processors so as to maintain a relatively even workload per processor. This creates a problem at the locations in the domain where adjacent points are not on the same processor. In order for the values at these points to be calculated, messages have to be exchanged between the corresponding processors. Without special adaptation, this will result in idle processors during part of the computation, and as the number of idle processors increases, the lower the effective speed improvement by using a parallel processor.

  6. BPR: Bayesian Personalized Ranking from Implicit Feedback

    CERN Document Server

    Rendle, Steffen; Gantner, Zeno; Schmidt-Thieme, Lars

    2012-01-01

    Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor (kNN). Even though these methods are designed for the item prediction task of personalized ranking, none of them is directly optimized for ranking. In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem. We also provide a generic learning algorithm for optimizing models with respect to BPR-Opt. The learning method is based on stochastic gradient descent with bootstrap sampling. We show how to apply our method to two state-of-the-art recommender models: matrix factorization and adaptive kNN. Our experiments indicate that for the task of p...

  7. [Recommendations for selecting antimicrobial agents for in vitro susceptibility studies using automatic and semiautomatic systems].

    Science.gov (United States)

    Cantón, Rafael; Alós, Juan Ignacio; Baquero, Fernando; Calvo, Jorge; Campos, José; Castillo, Javier; Cercenado, Emilia; Domínguez, M Angeles; Liñares, Josefina; López-Cerezo, Lorena; Marco, Francesc; Mirelis, Beatriz; Morosini, María-Isabel; Navarro, Ferran; Oliver, Antonio; Pérez-Trallero, Emilio; Torres, Carmen; Martínez-Martínez, Luis

    2007-01-01

    The number of clinical microbiology laboratories that have incorporated automatic susceptibility testing devices has increased in recent years. The majority of these systems determine MIC values using microdilution panels or specific cards, with grouping into clinical categories (susceptible, intermediate or resistant) and incorporate expert systems to infer resistance mechanisms. This document presents the recommendations of a group of experts designated by Grupo de Estudio de los Mecanismos de Acción y Resistencia a los Antimicrobianos (GEMARA, Study group on mechanisms of action and resistance to antimicrobial agents) and Mesa Española de Normalización de la Sensibilidad y Resistencia a los Antimicrobianos (MENSURA, Spanish Group for Normalizing Antimicrobial Susceptibility and Antimicrobial Resistance), with the aim of including antimicrobial agents and selecting concentrations for the susceptibility testing panels of automatic systems. The following have been defined: various antimicrobial categories (A: must be included in the study panel; B: inclusion is recommended; and C: inclusion is secondary, but may facilitate interpretative reading of the antibiogram) and groups (0: not used in therapeutics but may facilitate the detection of resistance mechanisms; 1: must be studied and always reported; 2: must be studied and selectively reported; 3: must be studied and reported at a second level; and 4: should be studied in urinary tract pathogens isolated in urine and other specimens). Recommended antimicrobial concentrations are adapted from the breakpoints established by EUCAST, CLSI and MENSURA. This approach will lead to more accurate susceptibility testing results with better detection of resistance mechanisms, and allowing to reach the clinical goal of the antibiogram.

  8. [Temporary recommendation for use on off-label baclofen: viewpoint of Prescribers of the CAMTEA system].

    Science.gov (United States)

    Rolland, Benjamin; Deheul, Sylvie; Danel, Thierry; Bence, Camille; Blanquart, Marie-Christine; Bonord, Alexandre; Semal, Robin; Briand, Thierry; Sochala, Michel; Dubocage, Christelle; Dupriez, François; Duquesne, Damien; Gibour, Bernard; Loosfeld, Xavier; Henebelle, Dorothée; Henon, Michael; Vernalde, Elodie; Matton, Christian; Bacquet, Jean-Eudes; Molmy, Lucie; Sarasy, François; Simioni, Nicolas; Richez, Cécile; Gentil-Spinosi, Laure; Vosgien, Véronique; Yguel, Jacques; Ledent, Thierry; Auffret, Marine; Wilquin, Maroussia; Ziolkowski, Danièle; Sochala, Michel; Gautier, Sophie; Bordet, Régis; Cottencin, Olivier

    2015-01-01

    The use of high dose baclofen for alcohol-dependence emerged in France from 2008 based on empirical findings, and is still off-label. However, due to the rapid increase in this prescribing practice, the French health authorities have decided to frame it using an extraordinary regulatory measure named "temporary recommendation for use" (TRU). Baclofen prescribers from CAMTEA, a regional team-based off-label system for supervising baclofen prescribing, which was developed much prior to the TRU, discuss herein the pros and cons of this measure and the applicability of its different aspects in the daily clinical practice.

  9. The Belgian Health System Performance Report 2012: snapshot of results and recommendations to policy makers.

    Science.gov (United States)

    Vrijens, France; Renard, Françoise; Jonckheer, Pascale; Van den Heede, Koen; Desomer, Anja; Van de Voorde, Carine; Walckiers, Denise; Dubois, Cécile; Camberlin, Cécile; Vlayen, Joan; Van Oyen, Herman; Léonard, Christian; Meeus, Pascal

    2013-09-01

    Following the commitments of the Tallinn Charter, Belgium publishes the second report on the performance of its health system. A set of 74 measurable indicators is analysed, and results are interpreted following the five dimensions of the conceptual framework: accessibility, quality of care, efficiency, sustainability and equity. All domains of care are covered (preventive, curative, long-term and end-of-life care), as well as health status and health promotion. For all indicators, national/regional values are presented with their evolution over time. Benchmarking to results of other EU-15 countries is also systematic. The policy recommendations represent the most important output of the report.

  10. Recommended engineering practice to enhance the EMI/EMP immunity of electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Wagner, C.L.; Feero, W.E. [Electric Research and Management, Inc., State College, PA (United States)

    1992-12-01

    Many papers and reports have been written on studies conducted by the Oak Ridge National Laboratory and investigations by others on the effect of high-altitude electromagnetic pulses (HEMP) on electric power systems. More than 100 of the published unclassified documents were reviewed with the objectives of: 1. summarizing the mitigation methods suggested in the documents and providing a subjective evaluation of each 2. discussing various standards . that presently apply to the effects of HEMP on utility systems and suggesting additions or modifications or new standards where deficiencies appear to exist; and 3. recommending future studies or actions to improve the utility response to HEMP. While all three components of HEMP were mentioned, only the early-time short-duration E{sub 1} pulse and the late-time long-duration E{sub 3} pulse were considered in detail; the E{sub 2} intermediate component was not considered to affect the power system significantly.

  11. Recommended engineering practice to enhance the EMI/EMP immunity of electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Wagner, C.L.; Feero, W.E. (Electric Research and Management, Inc., State College, PA (United States))

    1992-12-01

    Many papers and reports have been written on studies conducted by the Oak Ridge National Laboratory and investigations by others on the effect of high-altitude electromagnetic pulses (HEMP) on electric power systems. More than 100 of the published unclassified documents were reviewed with the objectives of: 1. summarizing the mitigation methods suggested in the documents and providing a subjective evaluation of each 2. discussing various standards . that presently apply to the effects of HEMP on utility systems and suggesting additions or modifications or new standards where deficiencies appear to exist; and 3. recommending future studies or actions to improve the utility response to HEMP. While all three components of HEMP were mentioned, only the early-time short-duration E[sub 1] pulse and the late-time long-duration E[sub 3] pulse were considered in detail; the E[sub 2] intermediate component was not considered to affect the power system significantly.

  12. Terminology of Polymers and Polymerization Processes in Dispersed Systems (IUPAC Recommendations 2011

    Directory of Open Access Journals (Sweden)

    Rogošić, M.

    2012-07-01

    Full Text Available A large group of industrially important polymerization processes is carried out in dispersed systems. These processes differ with respect to their physical nature, mechanism of particle formation, particle morphology, size, charge, types of interparticle interactions, and many other aspects. Polymer dispersions, and polymers derived from polymerization in dispersed systems,are used in diverse areas such as paints, adhesives, microelectronics, medicine, cosmetics, biotechnology, and others. Frequently, the same names are used for different processes and products or different names are used for the same processes and products. The document contains a list of recommended terms and definitions necessary for the unambiguous description of processes, products, parameters, and characteristic features relevant to polymers in dispersed systems.

  13. One to One Recommendation System in Apparel On-Line Shopping

    Science.gov (United States)

    Sekozawa, Teruji; Mitsuhashi, Hiroyuki; Ozawa, Yukio

    We propose an apparel online shopping site that the fashion adviser exists on the internet. The fashion adviser, who has detailed knowledge about the fashion in real shop, selects and coordinates the clothes of the customer's preference. However, the customer, who didn't have detailed knowledge about the fashion, was not able to choose the clothes suitable for the customer's preference from among the candidate of a large amount of clothes on a conventional apparel shopping site. Then, we compose the system that analyzes the customer's preference by the AHP technique, makes to the cluster by the correlation of clothes, and analyzes the market basket. As a result, this system can coordinate the clothes appropriate for the favor of an individual customer. Moreover, this system can propose the recommendation of other clothes based on past sales data.

  14. Graphical user interface for a remote medical monitoring system: U.S. Army medic recommendations.

    Science.gov (United States)

    Kaushik, Sangeeta; Tharion, William J

    2009-11-01

    We obtained recommendations for a graphical user interface (GUI) design for a new medical monitoring system. Data were obtained from 26 combat-experienced medics. Volunteers were briefed on the medical monitoring system. They then completed a questionnaire on background medical treatment experience, provided drawings on how and what information should be displayed on the GUI screens for use on a personal digital assistant, and participated in focus group sessions with four to seven medics per group to obtain group consensus on what information the GUI screens should contain. Detailed displays on seven screens provide the medical and situational awareness information medics need for triage decisions and for early processing of a casualty. The created GUI screens are a combination of object-based and text-based information using a color-coded system. Medics believed the information displayed with these GUI designs would improve treatment of casualties on the battlefield.

  15. Recommended Research Directions for Improving the Validation of Complex Systems Models.

    Energy Technology Data Exchange (ETDEWEB)

    Vugrin, Eric D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trucano, Timothy G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Finley, Patrick D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Flanagan, Tatiana Paz [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Naugle, Asmeret Bier [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Tsao, Jeffrey Y. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-02-01

    Improved validation for models of complex systems has been a primary focus over the past year for the Resilience in Complex Systems Research Challenge. This document describes a set of research directions that are the result of distilling those ideas into three categories of research -- epistemic uncertainty, strong tests, and value of information. The content of this document can be used to transmit valuable information to future research activities, update the Resilience in Complex Systems Research Challenge's roadmap, inform the upcoming FY18 Laboratory Directed Research and Development (LDRD) call and research proposals, and facilitate collaborations between Sandia and external organizations. The recommended research directions can provide topics for collaborative research, development of proposals, workshops, and other opportunities.

  16. System Performance Measurement Supports Design Recommendations for Solar Ventilation Preheat System (Brochure)

    Energy Technology Data Exchange (ETDEWEB)

    2011-08-01

    Technical briefing to report the outcomes of a data monitoring effort to determine the nature of solar vent preheat system performance problems at a U.S. military installation. The analysis reports up-to-date research and findings regarding system design, helping to clarify the issue as a factor of system design, rather than a shortcoming of SVP systems.

  17. Let´s go to the cinema! A movie recommender system for ephemeral groups of users

    Directory of Open Access Journals (Sweden)

    Guillermo Fernández

    2015-08-01

    Full Text Available Going to the cinema or watching television are social activities that generally take place in groups. In these cases, a recommender system for ephemeral groups of users is more suitable than (well-studied recommender systems for individuals. In this paper we present a recommendation system for groups of users that go to the cinema. The system uses the Slope One algorithm for computing individual predictions and the Multiplicative Utilitarian Strategy as a model to make a recommendation to an entire group. We show how we solved all practical aspects of the system; including its architecture and a mobile application for the service, the lack of user data (ramp-up and cold-start problems, the scaling fit of the group model strategy, and other improvements in order to reduce the response time. Finally, we validate the performance of the system with a set of experiments with 57 ephemeral groups.

  18. Necessary but Not Sufficient: Challenges to (Implicit) Theories of Educational Change--Reform in Nepal's Primary Education System

    Science.gov (United States)

    Khaniya, Tirth; Williams, James H.

    2004-01-01

    Educational quality reforms are undertaken in hopes that students in a higher quality education system will acquire more of the curriculum. However, the authors argue, there is no necessary connection between investments in educational quality and improved learning outcomes. A national assessment of grade 3 students in Nepal found few differences…

  19. [New recommendations concerning the fluoride content of toddler toothpaste - consequences for systemic application of fluoride].

    Science.gov (United States)

    König, K G

    2002-01-01

    preventive measures was much less, and a reduction of sugar consumption (by the way less than 10 % of what it was in 1970) seems to have been the least important factor. The new recommendations based on topical rather than systemic fluoride application are better for preventive, toxicological, psychological and didactic reasons and should be implemented as soon as possible.

  20. Implicit Memory in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    G. Latchford

    1993-01-01

    Full Text Available A number of neuropsychological studies have revealed that memory problems are relatively common in patients with multiple sclerosis (MS. It may be useful to compare MS with conditions such as Huntington's disease (HD, which have been referred to as subcortical dementia. A characteristic of these conditions may be an impairment in implicit (unconscious memory, but not in explicit (conscious memory. The present study examined the functioning of explicit and implicit memory in MS. Results showed that implicit memory was not significantly impaired in the MS subjects, and that they were impaired on recall but not recognition. A correlation was found between implicit memory performance and disability status in MS patients. Findings also suggest the possibility of long-term priming of implicit memory in the control subjects. The implications of these results are discussed.

  1. Multilevel Drift-Implicit Tau-Leap

    KAUST Repository

    Ben Hammouda, Chiheb

    2016-01-06

    The dynamics of biochemical reactive systems with small copy numbers of one or more reactant molecules is dominated by stochastic effects. For those systems, discrete state-space and stochastic simulation approaches were proved to be more relevant than continuous state-space and deterministic ones. In systems characterized by having simultaneously fast and slowtimescales, the existing discrete space-state stochastic path simulation methods such as the stochastic simulation algorithm (SSA) and the explicit tauleap method can be very slow. Implicit approximations were developed in the literature to improve numerical stability and provide efficient simulation algorithms for those systems. In this work, we propose an efficient Multilevel Monte Carlo method in the spirit of the work by Anderson and Higham (2012) that uses drift-implicit tau-leap approximations at levels where the explicit tauleap method is not applicable due to numerical stability issues. We present numerical examples that illustrate the performance of the proposed method.

  2. Intergroup anxiety effects on implicit racial evaluation and stereotyping.

    Science.gov (United States)

    Amodio, David M; Hamilton, Holly K

    2012-12-01

    How does intergroup anxiety affect the activation of implicit racial evaluations and stereotypes? Given the common basis of social anxiety and implicit evaluative processes in memory systems linked to classical conditioning and affect, we predicted that intergroup anxiety would amplify implicit negative racial evaluations. Implicit stereotyping, which is associated primarily with semantic memory systems, was not expected to increase as a function of intergroup anxiety. This pattern was observed among White participants preparing to interact with Black partners, but not those preparing to interact with White partners. These findings shed new light on how anxiety, often elicited in real-life intergroup interactions, can affect the operation of implicit racial biases, suggesting that intergroup anxiety has more direct implications for affective and evaluative forms of implicit bias than for implicit stereotyping. These findings also support a memory-systems model of the interplay between emotion and cognition in the context of social behavior. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  3. Systems for grading the quality of evidence and the strength of recommendations II: Pilot study of a new system

    Directory of Open Access Journals (Sweden)

    Oxman Andrew D

    2005-03-01

    Full Text Available Abstract Background Systems that are used by different organisations to grade the quality of evidence and the strength of recommendations vary. They have different strengths and weaknesses. The GRADE Working Group has developed an approach that addresses key shortcomings in these systems. The aim of this study was to pilot test and further develop the GRADE approach to grading evidence and recommendations. Methods A GRADE evidence profile consists of two tables: a quality assessment and a summary of findings. Twelve evidence profiles were used in this pilot study. Each evidence profile was made based on information available in a systematic review. Seventeen people were given instructions and independently graded the level of evidence and strength of recommendation for each of the 12 evidence profiles. For each example judgements were collected, summarised and discussed in the group with the aim of improving the proposed grading system. Kappas were calculated as a measure of chance-corrected agreement for the quality of evidence for each outcome for each of the twelve evidence profiles. The seventeen judges were also asked about the ease of understanding and the sensibility of the approach. All of the judgements were recorded and disagreements discussed. Results There was a varied amount of agreement on the quality of evidence for the outcomes relating to each of the twelve questions (kappa coefficients for agreement beyond chance ranged from 0 to 0.82. However, there was fair agreement about the relative importance of each outcome. There was poor agreement about the balance of benefits and harms and recommendations. Most of the disagreements were easily resolved through discussion. In general we found the GRADE approach to be clear, understandable and sensible. Some modifications were made in the approach and it was agreed that more information was needed in the evidence profiles. Conclusion Judgements about evidence and recommendations are

  4. Services Recommendation System based on Heterogeneous Network Analysis in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Junping Dong

    2014-04-01

    Full Text Available Resources are provided mainly in the form of services in cloud computing. In the distribute environment of cloud computing, how to find the needed services efficiently and accurately is the most urgent problem in cloud computing. In cloud computing, services are the intermediary of cloud platform, services are connected by lots of service providers and requesters and construct the complex heterogeneous network. The traditional recommendation systems only consider the functional and non-functional requirements of services but ignore the links between providers and requesters of service, which result to the service position is not accurate. Focus on the problems, this study intends to model the relationship of the cloud services participants with the format of heterogeneous information network, which intend to mine the hidden relationships between services participants in the cloud computing environment. In theoretical research, we proposed a cloud service heterogeneous network extraction and automatic maintenance model, proposed a new service recommendation system based on heterogeneous service network ranking and clustering.

  5. SYNAISTHISI: an IoT-powered smart visitor management and cognitive recommendations system

    Science.gov (United States)

    Thanos, Giorgos Konstandinos; Karafylli, Christina; Karafylli, Maria; Zacharakis, Dimitris; Papadimitriou, Apostolis; Dimitros, Kostantinos; Kanellopoulou, Konstantina; Kyriazanos, Dimitris M.; Thomopoulos, Stelios C. A.

    2016-05-01

    Location-based and navigation services are really needed to help visitors and audience of big events, complex buildings, shopping malls, airports and large companies. However, the lack of GPS and proper mapping indoors usually renders location-based applications and services useless or simply not applicable in such environments. SYNAISTHISI introduces a mobile application for smartphones which offers navigation capabilities outside and inside buildings and through multiple floor levels. The application comes together with a suite of helpful services, including personalized recommendations, visit/event management and a helpful search functionality in order to navigate to a specific location, event or person. As the user finds his way towards his destination, NFC-enabled checkpoints and bluetooth beacons assist him, while offering re-routing, check-in/out capabilities and useful information about ongoing meetings and nearby events. The application is supported by a back-end GIS system which can provide a broad and clear view to event organizers, campus managers and field personnel for purposes of event logistics, safety and security. SYNAISTHISI system comes with plenty competitive advantages including (a) Seamless Navigation as users move between outdoor and indoor areas and different floor levels by using innovative routing algorithms, (b) connection to and powered by IoT platform, for localization and real-time information feedback, (c) dynamic personalized recommendations based on user profile, location and real-time information provided by the IoT platform and (d) Indoor localization without the need for expensive infrastructure and installations.

  6. EU Country Specific Recommendations for health systems in the European Semester process: trends, discourse and predictors.

    Science.gov (United States)

    Azzopardi-Muscat, Natasha; Clemens, Timo; Stoner, Deborah; Brand, Helmut

    2015-03-01

    In the framework of "Europe 2020", European Union Member States are subject to a new system of economic monitoring and governance known as the European Semester. This paper seeks to analyse the way in which national health systems are being influenced by EU institutions through the European Semester. A content analysis of the Country Specific Recommendations (CSRs) for the years 2011, 2012, 2013 and 2014 was carried out. This confirmed an increasing trend for health systems to feature in CSRs which tend to be framed in the discourse on sustainability of public finances rather than that of social inclusion with a predominant focus on the policy objective of sustainability. The likelihood of obtaining a health CSRs was tested against a series of financial health system performance indicators and general government finance indicators. The odds ratio of obtaining a health CSR increased slightly with the increase in level of general Government debt, with an OR 1.02 (CI: 1.01, 1.03; p=0.007) and decreased with an increased public health expenditure/total health expenditure ratio, with an OR 0.89 (CI: 0.84, 0.96; p=0.001). The European Semester process is a relatively new process that is influencing health systems in the European Union. The effect of this process on health systems merits further attention. Health stakeholders should seek to engage more closely with this process which if steered appropriately could also present opportunities for health system reform.

  7. Task 9 recommended practice guides - Executive summaries. Financing mechanisms for solar home systems in developing countries

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2002-09-15

    This Practice Guide issued by the Photovoltaic Power Systems (PVPS) group of the International Energy Agency (IEA) summarises how insufficient financing, the low incomes of the potential clients in remote rural areas and the high initial investment costs for the Solar Home System (SHS) are the factors responsible for insufficient progress in this area. The findings of the study such as access to finance, subsidies, formal and informal intermediaries and alternative financing solutions are discussed. SHS operating costs, possible higher priorities for other commodities and other market-driven factors are discussed. The report notes that most other reports concentrate more on technical and institutional rather than on the underlying financing schemes and associated data. Recommendations made deal with political aspects as well as technical, financial and awareness issues.

  8. Corruption in Education Systems: A Review of Practices, Causes, Effects and Recommendations

    Directory of Open Access Journals (Sweden)

    Sergio Cárdenas Denham

    2012-11-01

    Full Text Available Achieving higher levels of transparency and accountability in education systems is essential for attaining an adequate distribution of educational opportunities. Studies of corruption in education systems are important since they can contribute to increasing public awareness of the harmful effects of corruption and promote political support for the implementation of anti-corruption initiatives in education. This paper describes a typology of corrupt practices and develops a classification for the findings reported in the literature on corruption in education, such as causes, consequences and recommendations, as well as a classification of the methodologies applied to the study of corrupt practices. Finally, it describes the possible implications of these findings for policymakers.

  9. 个性化的旅游推荐系统%Ontology-based travel recommendation system

    Institute of Scientific and Technical Information of China (English)

    胡纳纳; 李琳琳; 武尚

    2013-01-01

    In order to retrieve the activities which are most interested by the tourist from a large amount of information, the paper designs a recommended system which is combined with the ontology and geographic information system. Through constructing the domain ontology, the interested activity of the user could correspond to one or more ontology concepts. In addition, by taking the factors of population data, travel motivations, interaction of the user with the system, user' s rating into account, applying many kinds of recommendation technologies and artificial intelligence algorithms, the system could retrieves the activities and achieves semantic query effectively.%为了从大量的信息中检索出符合旅游者感兴趣的活动,文中设计了一种本体和地理信息系统相结合的推荐系统.该系统通过构建领域本体,使用户感兴趣的活动与一个或多个本体的概念相对应并充分考虑到人口数据、旅游动机、用户与系统的交互作用、用户提供的评级等因素,综合利用各种推荐技术和人工智能算法,系统检索出用户喜欢的旅游活动,有效地实现了语义化查询.

  10. A Data Management System Integrating Web-based Training and Randomized Trials: Requirements, Experiences and Recommendations.

    Science.gov (United States)

    Muroff, Jordana; Amodeo, Maryann; Larson, Mary Jo; Carey, Margaret; Loftin, Ralph D

    2011-01-01

    This article describes a data management system (DMS) developed to support a large-scale randomized study of an innovative web-course that was designed to improve substance abuse counselors' knowledge and skills in applying a substance abuse treatment method (i.e., cognitive behavioral therapy; CBT). The randomized trial compared the performance of web-course-trained participants (intervention group) and printed-manual-trained participants (comparison group) to determine the effectiveness of the web-course in teaching CBT skills. A single DMS was needed to support all aspects of the study: web-course delivery and management, as well as randomized trial management. The authors briefly reviewed several other systems that were described as built either to handle randomized trials or to deliver and evaluate web-based training. However it was clear that these systems fell short of meeting our needs for simultaneous, coordinated management of the web-course and the randomized trial. New England Research Institute's (NERI) proprietary Advanced Data Entry and Protocol Tracking (ADEPT) system was coupled with the web-programmed course and customized for our purposes. This article highlights the requirements for a DMS that operates at the intersection of web-based course management systems and randomized clinical trial systems, and the extent to which the coupled, customized ADEPT satisfied those requirements. Recommendations are included for institutions and individuals considering conducting randomized trials and web-based training programs, and seeking a DMS that can meet similar requirements.

  11. mHealth: A Design of an Exercise Recommendation System for the Android Operating System

    Directory of Open Access Journals (Sweden)

    Pongpisit WUTTIDITTACHOTTI

    2014-05-01

    Full Text Available For healthiness and wellness, exercising is one of the key factors. Therefore, this paper aims to present the first phase of a mobile health application developed to recommend healthcare support referring to exercises on an Android smartphone. This application has been designed to provide exercise advice depending on Body Mass Index (BMI, Basal Metabolic Rate (BMR and the energy used in each activity or sport (e.g. aerobic dancing, cycling, jogging working and swimming. Also, this application has been designed to present special exercise advice for patients with health issues. Moreover, it has been designed to store information in a database and to have the ability to produce reports to users. After designing, this proposed mHealth application has been evaluated by 30 subjects who have computer programming skills. It has been found that all diagrams, including use the case diagram, sequence diagrams and overall were assessed as ‘good’, except the part of user interfaces that was assessed as ‘fair’. Therefore, this design can be used to implement in the next phase of this application development with minor revision concerning the user interfaces.

  12. Implementing an excellence in teaching recognition system: needs analysis and recommendations.

    Science.gov (United States)

    Schindler, Nancy; Corcoran, Julia C; Miller, Megan; Wang, Chih-Hsiung; Roggin, Kevin; Posner, Mitchell; Fryer, Jonathan; DaRosa, Debra A

    2013-01-01

    Teaching awards have been suggested to serve a variety of purposes. The specific characteristics of teaching awards and the associated effectiveness at achieving planned purposes are poorly understood. A needs analysis was performed to inform recommendations for an Excellence in Teaching Recognition System to meet the needs of surgical education leadership. We performed a 2-part needs analysis beginning with a review of the literature. We then, developed, piloted, and administered a survey instrument to General Surgery program leaders. The survey examined the features and perceived effectiveness of existing teaching awards systems. A multi-institution committee of program directors, clerkship directors, and Vice-Chairs of education then met to identify goals and develop recommendations for implementation of an "Excellence in Teaching Recognition System." There is limited evidence demonstrating effectiveness of existing teaching awards in medical education. Evidence supports the ability of such awards to demonstrate value placed on teaching, to inspire faculty to teach, and to contribute to promotion. Survey findings indicate that existing awards strive to achieve these purposes and that educational leaders believe awards have the potential to do this and more. Leaders are moderately satisfied with existing awards for providing recognition and demonstrating value placed on teaching, but they are less satisfied with awards for motivating faculty to participate in teaching or for contributing to promotion. Most departments and institutions honor only a few recipients annually. There is a paucity of literature addressing teaching recognition systems in medical education and little evidence to support the success of such systems in achieving their intended purposes. The ability of awards to affect outcomes such as participation in teaching and promotion may be limited by the small number of recipients for most existing awards. We propose goals for a Teaching Recognition

  13. Detection of Abnormal Item Based on Time Intervals for Recommender Systems

    Directory of Open Access Journals (Sweden)

    Min Gao

    2014-01-01

    Full Text Available With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from “shilling” attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ2. We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users.

  14. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN classification method

    Directory of Open Access Journals (Sweden)

    D.A. Adeniyi

    2016-01-01

    Full Text Available The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

  15. Detection of abnormal item based on time intervals for recommender systems.

    Science.gov (United States)

    Gao, Min; Yuan, Quan; Ling, Bin; Xiong, Qingyu

    2014-01-01

    With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from "shilling" attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ(2)). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users.

  16. Study report recommendations for the next generation Range Safety System (RSS) Integrated Receiver/Decoder (IRD)

    Science.gov (United States)

    Crosby, Robert H.

    1992-01-01

    The Integrated Receiver/Decoder (IRD) currently used on the Space Shuttle was designed in the 1980 and prior time frame. Over the past 12 years, several parts have become obsolete or difficult to obtain. As directed by the Marshall Space Flight Center, a primary objective is to investigate updating the IRD design using the latest technology subsystems. To take advantage of experience with the current designs, an analysis of failures and a review of discrepancy reports, material review board actions, scrap, etc. are given. A recommended new design designated as the Advanced Receiver/Decoder (ARD) is presented. This design uses the latest technology components to simplify circuits, improve performance, reduce size and cost, and improve reliability. A self-test command is recommended that can improve and simplify operational procedures. Here, the new design is contrasted with the old. Possible simplification of the total Range Safety System is discussed, as is a single-step crypto technique that can improve and simplify operational procedures.

  17. Explaining phenomena of first and second language acquisition with the constructs of implicit and explicit learning: The virtues and pitfalls of a two-system view

    NARCIS (Netherlands)

    Hulstijn, J.H.; Rebuschat, P.

    2015-01-01

    This chapter examines to what extent Krashen’s (1981) distinction between acquired (implicit) and learned (explicit) knowledge can be upheld from a usage-based view on first and second language learning and in the light of recent advancement in (neuro)cognitive research on artificial grammar learnin

  18. E-commerce Website Recommender System Based on Dissimilarity and Association Rule

    OpenAIRE

    MingWang Zhang; ShuWen Yang; LiFeng Zhang

    2013-01-01

    By analyzing the current electronic commerce recommendation algorithm analysis, put forward a kind to use dissimilarity clustering and association recommendation algorithm, the algorithm realized web website shopping user data clustering by use of the dissimilarity, and then use the association rules algorithm for clustering results of association recommendation, experiments show that the algorithm compared with traditional clustering association algorithm of iteration times decrease, improve...

  19. E-commerce Website Recommender System Based on Dissimilarity and Association Rule

    Directory of Open Access Journals (Sweden)

    MingWang Zhang

    2013-07-01

    Full Text Available By analyzing the current electronic commerce recommendation algorithm analysis, put forward a kind to use dissimilarity clustering and association recommendation algorithm, the algorithm realized web website shopping user data clustering by use of the dissimilarity, and then use the association rules algorithm for clustering results of association recommendation, experiments show that the algorithm compared with traditional clustering association algorithm of iteration times decrease, improve operational efficiency, to prove the method by use of the actual users purchase the recommended, and evidence of the effectiveness of the algorithm in recommendation.  

  20. Results and recommendations from an intercomparison of six Hygroscopicity-TDMA systems

    Directory of Open Access Journals (Sweden)

    A. Massling

    2010-02-01

    Full Text Available The performance of six custom-built Hygrocopicity-Tandem Differential Mobility Analyzers (H-TDMA systems was investigated in the frame of an international calibration and intercomparison workshop held in Leipzig, February 2006. The goal of the workshop was to harmonize H-TDMA measurements and develop recommendations for atmospheric measurements and their data evaluation. The H-TDMA systems were compared in terms of the sizing of dry particles, relative humidity (RH uncertainty and consistency in determination of number fractions of different hygroscopic particle groups. The experiments were performed in an air-conditioned laboratory using ammonium sulfate particles or an external mixture of ammonium sulfate and soot particles.

    The sizing of dry particles of the six H-TDMA systems was within 0.2 to 4.2% of the selected particle diameter depending on investigated size and individual system.

    With regard to RH uncertainties, the H-TDMA systems showed deviations up to 4.5% RH from the set point at RH=90% investigating the hygroscopic growth of ammonium sulfate particles and comparing the results with theory.

    The evaluation of number fractions investigating an externally mixed aerosol delivered differences up to +/−8% in calculated number fraction for one and the same aerosol type.

    We analysed the datasets of the different H-TDMAs with one fitting routine to investigate differences caused by the different data evaluation procedures. The results showed that the differences were reduced from +12/−13% to +8/−6%. We can conclude here that a common data evaluation procedure to determine the number fraction of externally mixed aerosols will improve the comparability of H-TDMA measurements.

    We finally recommend, to ensure a good calibration of all flow, temperature and RH sensors in the systems. It is most important to thermally insulate the RH control unit and the second DMA and to monitor those

  1. Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems

    Directory of Open Access Journals (Sweden)

    Patricia Noriega-Vivas

    2012-06-01

    Full Text Available Predicting users’ next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.

  2. MapReduce based computation of the diffusion method in recommender systems

    Institute of Scientific and Technical Information of China (English)

    彭飞

    2016-01-01

    The performance of existing diffusion-based algorithms in recommender systems is still limited by the processing ability of a single computer .In order to conduct the diffusion computation on large data sets, a parallel implementation of the classic diffusion method on the MapReduce framework is proposed.At first, the diffusion computation is transformed from a summation format to a cascade matrix multiplication format , and then , a parallel matrix multiplication algorithm based on dynamic vector is proposed to reduce the CPU and I/O cost on the MapReduce framework , which can also be applied to other parallel matrix multiplication scenarios .Then, block partitioning is used to further improve the performance , while the order of matrix multiplication is also taken into consideration . Experiments on different kinds of data sets have verified the efficiency of the proposed method .

  3. Recommendation systems: a joint analysis of technical aspects with marketing implications

    CERN Document Server

    Michalis, Vafopoulos

    2011-01-01

    In 2010, Web users ordered, only in Amazon, 73 items per second and massively contribute reviews about their consuming experience. As the Web matures and becomes social and participatory, collaborative filters are the basic complement in searching online information about people, events and products. In Web 2.0, what connected consumers create is not simply content (e.g. reviews) but context. This new contextual framework of consumption emerges through the aggregation and collaborative filtering of personal preferences about goods in the Web in massive scale. More importantly, facilitates connected consumers to search and navigate the complex Web more effectively and amplifies incentives for quality. The objective of the present article is to jointly review the basic stylized facts of relevant research in recommendation systems in computer and marketing studies in order to share some common insights. After providing a comprehensive definition of goods and Users in the Web, we describe a classification of reco...

  4. Design and Implementation of a Threaded Search Engine for Tour Recommendation Systems

    Science.gov (United States)

    Lee, Junghoon; Park, Gyung-Leen; Ko, Jin-Hee; Shin, In-Hye; Kang, Mikyung

    This paper implements a threaded scan engine for the O(n!) search space and measures its performance, aiming at providing a responsive tour recommendation and scheduling service. As a preliminary step of integrating POI ontology, mobile object database, and personalization profile for the development of new vehicular telematics services, this implementation can give a useful guideline to design a challenging and computation-intensive vehicular telematics service. The implemented engine allocates the subtree to the respective threads and makes them run concurrently exploiting the primitives provided by the operating system and the underlying multiprocessor architecture. It also makes it easy to add a variety of constraints, for example, the search tree is pruned if the cost of partial allocation already exceeds the current best. The performance measurement result shows that the service can run even in the low-power telematics device when the number of destinations does not exceed 15, with an appropriate constraint processing.

  5. A recommendation module to help teachers build courses through the Moodle Learning Management System

    Science.gov (United States)

    Limongelli, Carla; Lombardi, Matteo; Marani, Alessandro; Sciarrone, Filippo; Temperini, Marco

    2016-01-01

    In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.

  6. Recommendations for the design and the installation of large laser scanning microscopy systems

    Science.gov (United States)

    Helm, P. Johannes

    2012-03-01

    Laser Scanning Microscopy (LSM) has since the inventions of the Confocal Scanning Laser Microscope (CLSM) and the Multi Photon Laser Scanning Microscope (MPLSM) developed into an essential tool in contemporary life science and material science. The market provides an increasing number of turn-key and hands-off commercial LSM systems, un-problematic to purchase, set up and integrate even into minor research groups. However, the successful definition, financing, acquisition, installation and effective use of one or more large laser scanning microscopy systems, possibly of core facility character, often requires major efforts by senior staff members of large academic or industrial units. Here, a set of recommendations is presented, which are helpful during the process of establishing large systems for confocal or non-linear laser scanning microscopy as an effective operational resource in the scientific or industrial production process. Besides the description of technical difficulties and possible pitfalls, the article also illuminates some seemingly "less scientific" processes, i.e. the definition of specific laboratory demands, advertisement of the intention to purchase one or more large systems, evaluation of quotations, establishment of contracts and preparation of the local environment and laboratory infrastructure.

  7. Systems Perspective of Amazon Mechanical Turk for Organizational Research: Review and Recommendations.

    Science.gov (United States)

    Keith, Melissa G; Tay, Louis; Harms, Peter D

    2017-01-01

    Amazon Mechanical Turk (MTurk) is becoming a prevalent source of quick and cost effective data for organizational research, but there are questions about the appropriateness of the platform for organizational research. To answer these questions, we conducted an integrative review based on 75 papers evaluating the MTurk platform and 250 MTurk samples used in organizational research. This integrative review provides four contributions: (1) we analyze the trends associated with the use of MTurk samples in organizational research; (2) we develop a systems perspective (recruitment system, selection system, and work management system) to synthesize and organize the key factors influencing data collected on MTurk that may affect generalizability and data quality; (3) within each factor, we also use available MTurk samples from the organizational literature to analyze key issues (e.g., sample characteristics, use of attention checks, payment); and (4) based on our review, we provide specific recommendations and a checklist for data reporting in order to improve data transparency and enable further research on this issue.

  8. Recommendations for an Executive Information System (EIS) for the NASA Accounting and Financial Information System (NAFIS)

    Science.gov (United States)

    Goss, Ernest Preston

    1991-01-01

    The objectives were to: (1) survey state-of-the-art computing architectures, tools, and technologies for implementing an Executive Information System (EIS); (2) review MSFC capabilities and efforts in developing an EIS for Shuttle Projects Office and the Payloads Project Office; (3) review management reporting requirements for the NASA Accounting and Financial Information System (NAFIS) Project in the areas of cost, schedule, and technical performance, and insure that the EIS fully supports these requirements; and (4) develop and implement a pilot concept for a NAFIS EIS. A summary of the findings of this work is presented.

  9. Survey of Collaborative Filtering Recommender Systems%协同过滤推荐系统研究综述

    Institute of Scientific and Technical Information of China (English)

    杨强; 杨有; 余春君

    2015-01-01

    In the era of big data, internetusers are difficult to find the information that they need from the vast information,recommender systems can well solve the problem of information overload, the collaborative filtering recommendation technology is the most widely used and suc-cessful. Classifies the collaborative filtering recommender systems, describes the main algorithms of collaborative filtering recommender systems, presents the evaluation of recommender systems, finally, summarizes the remaining problems of recommender systems.%在大数据时代,网络用户很难从海量信息当中找到自己需求的信息,推荐系统可以很好地解决信息过载问题,协同过滤推荐是应用最广泛和最成功的推荐技术。对协同过滤推荐系统进行分类,描述协同过滤推荐系统的主要算法和基本思想,介绍推荐系统的评价指标,总结推荐系统仍然存在的问题。

  10. Results and recommendations from an intercomparison of six Hygroscopicity-TDMA systems

    Directory of Open Access Journals (Sweden)

    A. Massling

    2011-03-01

    Full Text Available The performance of six custom-built Hygrocopicity-Tandem Differential Mobility Analyser (H-TDMA systems was investigated in the frame of an international calibration and intercomparison workshop held in Leipzig, February 2006. The goal of the workshop was to harmonise H-TDMA measurements and develop recommendations for atmospheric measurements and their data evaluation. The H-TDMA systems were compared in terms of the sizing of dry particles, relative humidity (RH uncertainty, and consistency in determination of number fractions of different hygroscopic particle groups. The experiments were performed in an air-conditioned laboratory using ammonium sulphate particles or an external mixture of ammonium sulphate and soot particles.

    The sizing of dry particles of the six H-TDMA systems was within 0.2 to 4.2% of the selected particle diameter depending on investigated size and individual system. Measurements of ammonium sulphate aerosol found deviations equivalent to 4.5% RH from the set point of 90% RH compared to results from previous experiments in the literature. Evaluation of the number fraction of particles within the clearly separated growth factor modes of a laboratory generated externally mixed aerosol was done. The data from the H-TDMAs was analysed with a single fitting routine to investigate differences caused by the different data evaluation procedures used for each H-TDMA. The differences between the H-TDMAs were reduced from +12/−13% to +8/−6% when the same analysis routine was applied. We conclude that a common data evaluation procedure to determine number fractions of externally mixed aerosols will improve the comparability of H-TDMA measurements.

    It is recommended to ensure proper calibration of all flow, temperature and RH sensors in the systems. It is most important to thermally insulate the aerosol humidification unit and the second DMA and to monitor these temperatures to an accuracy of 0.2 °C. For the

  11. Results and recommendations from an intercomparison of six Hygroscopicity-TDMA systems

    Science.gov (United States)

    Massling, A.; Niedermeier, N.; Hennig, T.; Fors, E. O.; Swietlicki, E.; Ehn, M.; Hämeri, K.; Villani, P.; Laj, P.; Good, N.; McFiggans, G.; Wiedensohler, A.

    2011-03-01

    The performance of six custom-built Hygrocopicity-Tandem Differential Mobility Analyser (H-TDMA) systems was investigated in the frame of an international calibration and intercomparison workshop held in Leipzig, February 2006. The goal of the workshop was to harmonise H-TDMA measurements and develop recommendations for atmospheric measurements and their data evaluation. The H-TDMA systems were compared in terms of the sizing of dry particles, relative humidity (RH) uncertainty, and consistency in determination of number fractions of different hygroscopic particle groups. The experiments were performed in an air-conditioned laboratory using ammonium sulphate particles or an external mixture of ammonium sulphate and soot particles. The sizing of dry particles of the six H-TDMA systems was within 0.2 to 4.2% of the selected particle diameter depending on investigated size and individual system. Measurements of ammonium sulphate aerosol found deviations equivalent to 4.5% RH from the set point of 90% RH compared to results from previous experiments in the literature. Evaluation of the number fraction of particles within the clearly separated growth factor modes of a laboratory generated externally mixed aerosol was done. The data from the H-TDMAs was analysed with a single fitting routine to investigate differences caused by the different data evaluation procedures used for each H-TDMA. The differences between the H-TDMAs were reduced from +12/-13% to +8/-6% when the same analysis routine was applied. We conclude that a common data evaluation procedure to determine number fractions of externally mixed aerosols will improve the comparability of H-TDMA measurements. It is recommended to ensure proper calibration of all flow, temperature and RH sensors in the systems. It is most important to thermally insulate the aerosol humidification unit and the second DMA and to monitor these temperatures to an accuracy of 0.2 °C. For the correct determination of external mixtures

  12. Recommendation Systems for Geoscience Data Portals Built by Analyzing Usage Patterns

    Science.gov (United States)

    Crosby, C.; Nandigam, V.; Baru, C.

    2009-04-01

    Since its launch five years ago, the National Science Foundation-funded GEON Project (www.geongrid.org) has been providing access to a variety of geoscience data sets such as geologic maps and other geographic information system (GIS)-oriented data, paleontologic databases, gravity and magnetics data and LiDAR topography via its online portal interface. In addition to data, the GEON Portal also provides web-based tools and other resources that enable users to process and interact with data. Examples of these tools include functions to dynamically map and integrate GIS data, compute synthetic seismograms, and to produce custom digital elevation models (DEMs) with user defined parameters such as resolution. The GEON portal built on the Gridsphere-portal framework allows us to capture user interaction with the system. In addition to the site access statistics captured by tools like Google Analystics which capture hits per unit time, search key words, operating systems, browsers, and referring sites, we also record additional statistics such as which data sets are being downloaded and in what formats, processing parameters, and navigation pathways through the portal. With over four years of data now available from the GEON Portal, this record of usage is a rich resource for exploring how earth scientists discover and utilize online data sets. Furthermore, we propose that this data could ultimately be harnessed to optimize the way users interact with the data portal, design intelligent processing and data management systems, and to make recommendations on algorithm settings and other available relevant data. The paradigm of integrating popular and commonly used patterns to make recommendations to a user is well established in the world of e-commerce where users receive suggestions on books, music and other products that they may find interesting based on their website browsing and purchasing history, as well as the patterns of fellow users who have made similar

  13. Implicitization of rational maps

    CERN Document Server

    Botbol, Nicolas

    2011-01-01

    Motivated by the interest in computing explicit formulas for resultants and discriminants initiated by B\\'ezout, Cayley and Sylvester in the eighteenth and nineteenth centuries, and emphasized in the latest years due to the increase of computing power, we focus on the implicitization of hypersurfaces in several contexts. Our approach is based on the use of linear syzygies by means of approximation complexes, following [Bus\\'e Jouanolou 03], where they develop the theory for a rational map $f:P^{n-1}\\dashrightarrow P^n$. Approximation complexes were first introduced by Herzog, Simis and Vasconcelos in [Herzog Simis Vasconcelos 82] almost 30 years ago. The main obstruction for this approximation complex-based method comes from the bad behavior of the base locus of $f$. Thus, it is natural to try different compatifications of $A^{n-1}$, that are better suited to the map $f$, in order to avoid unwanted base points. With this purpose, in this thesis we study toric compactifications $T$ for $A^{n-1}$. We provide re...

  14. Analysis of clustering algorithm on recommendation pool in recommendation system%推荐系统中推荐池的聚类算法分析

    Institute of Scientific and Technical Information of China (English)

    董祥和; 张春光

    2011-01-01

    Aimed at the problems of low real-time performance and low recommendation accuracy existing in expanding personalized recommendation system, a modified k-means clustering algorithm is put forward. It can compress recommendation pool. Kruskal algorithm is used with user difference evaluation on item sort to produce the first centers, and makes the first centers be close to class centers, and then gets clusters with high accuracy. The experimental results show that the improved k-means clustering algorithm can find more neighbors from minimal space than traditional k-means clustering, and improve efficiency and accuracy of finding neighbors.%针对个性化推荐系统规模扩大而引起的实时性差、推荐精度较低等问题,提出了改进的k-均值用户聚类算法,实现对推荐系统中推荐池的压缩,将用户在不同项目簇上的评价差异作为用户距离,采用kruskal最小生成树算法生成初始聚类中心,使得初始中心靠近类中心,这样得到的聚类更符合实际.进行了算法改进前后的实验对比,结果表明,改进的聚类算法可以在更小的用户空间内搜索到更多的邻居用户,提高了查找用户最近邻居的效率和精度.

  15. Electromagnetic pulse research on electric power systems: Program summary and recommendations

    Energy Technology Data Exchange (ETDEWEB)

    Barnes, P.R.; McConnell, B.W.; Van Dyke, J.W. (Oak Ridge National Lab., TN (United States)); Tesche, F.M. (Tesche (F.M.), Dallas, TX (United States)); Vance, E.F. (Vance (E.F.), Fort Worth, TX (United States))

    1993-01-01

    A single nuclear detonation several hundred kilometers above the central United States will subject much of the nation to a high-altitude electromagnetic pulse (BENT). This pulse consists of an intense steep-front, short-duration transient electromagnetic field, followed by a geomagnetic disturbance with tens of seconds duration. This latter environment is referred to as the magnetohydrodynamic electromagnetic pulse (NMENT). Both the early-time transient and the geomagnetic disturbance could impact the operation of the nation's power systems. Since 1983, the US Department of Energy has been actively pursuing a research program to assess the potential impacts of one or more BENT events on the nation's electric energy supply. This report summarizes the results of that program and provides recommendations for enhancing power system reliability under HENT conditions. A nominal HENP environment suitable for assessing geographically large systems was developed during the program and is briefly described in this report. This environment was used to provide a realistic indication of BEMP impacts on electric power systems. It was found that a single high-altitude burst, which could significantly disturb the geomagnetic field, may cause the interconnected power network to break up into utility islands with massive power failures in some areas. However, permanent damage would be isolated, and restoration should be possible within a few hours. Multiple bursts would likely increase the blackout areas, component failures, and restoration time. However, a long-term blackout of many months is unlikely because major power system components, such as transformers, are not likely to be damaged by the nominal HEND environment. Moreover, power system reliability, under both HENT and normal operating conditions, can be enhanced by simple, and often low cost, modifications to current utility practices.

  16. Recommendations for autonomous underway pCO 2 measuring systems and data-reduction routines

    Science.gov (United States)

    Pierrot, Denis; Neill, Craig; Sullivan, Kevin; Castle, Robert; Wanninkhof, Rik; Lüger, Heike; Johannessen, Truls; Olsen, Are; Feely, Richard A.; Cosca, Catherine E.

    2009-04-01

    In order to facilitate the collection of high quality and uniform surface water pCO 2 data, an underway pCO 2 instrument has been designed based on community input and is now commercially available. Along with instrumentation, agreements were reached on data reduction and quality control that can be easily applied to data from these systems by using custom-made freeware. This new automated underway pCO 2 measuring system is designed to be accurate to within 0.1 μatm for atmospheric pCO 2 measurements and to within 2 μatm for seawater pCO 2, targeted by the scientific community to constrain the regional air-sea CO 2 fluxes to 0.2 Pg C year -1. The procedure to properly reduce the underway pCO 2 data and perform the steps necessary for calculation of the fugacity of CO 2 from the measurements is described. This system is now widely used by the scientific community on many different types of ships. Combined with the recommended data-reduction procedures, it will facilitate producing data sets that will significantly decrease the uncertainty currently present in estimates of air-sea CO 2 fluxes.

  17. Bankruptcy as Implicit Health Insurance

    OpenAIRE

    Neale Mahoney

    2012-01-01

    This paper examines the interaction between health insurance and the implicit insurance that people have because they can file (or threaten to file) for bankruptcy. With a simple model that captures key institutional features, I demonstrate that the financial risk from medical shocks is capped by the assets that could be seized in bankruptcy. For households with modest seizable assets, this implicit “bankruptcy insurance” can crowd out conventional health insurance. I test these predictions u...

  18. An advanced implicit solver for MHD

    Science.gov (United States)

    Udrea, Bogdan

    A new implicit algorithm has been developed for the solution of the time-dependent, viscous and resistive single fluid magnetohydrodynamic (MHD) equations. The algorithm is based on an approximate Riemann solver for the hyperbolic fluxes and central differencing applied on a staggered grid for the parabolic fluxes. The algorithm employs a locally aligned coordinate system that allows the solution to the Riemann problems to be solved in a natural direction, normal to cell interfaces. The result is an original scheme that is robust and reduces the complexity of the flux formulas. The evaluation of the parabolic fluxes is also implemented using a locally aligned coordinate system, this time on the staggered grid. The implicit formulation employed by WARP3 is a two level scheme that was applied for the first time to the single fluid MHD model. The flux Jacobians that appear in the implicit scheme are evaluated numerically. The linear system that results from the implicit discretization is solved using a robust symmetric Gauss-Seidel method. The code has an explicit mode capability so that implementation and test of new algorithms or new physics can be performed in this simpler mode. Last but not least the code was designed and written to run on parallel computers so that complex, high resolution runs can be per formed in hours rather than days. The code has been benchmarked against analytical and experimental gas dynamics and MHD results. The benchmarks consisted of one-dimensional Riemann problems and diffusion dominated problems, two-dimensional supersonic flow over a wedge, axisymmetric magnetoplasmadynamic (MPD) thruster simulation and three-dimensional supersonic flow over intersecting wedges and spheromak stability simulation. The code has been proven to be robust and the results of the simulations showed excellent agreement with analytical and experimental results. Parallel performance studies showed that the code performs as expected when run on parallel

  19. The Army Selected Reserve Dental Readiness System: overview, assessment, and recommendations.

    Science.gov (United States)

    Honey, James R

    2013-06-01

    The Army Selected Reserve Dental Readiness System (ASDRS) is a key dental program directed by the Assistant Secretary of the Army (Manpower & Reserve Affairs) starting in fiscal year 09. The Army National Guard and Army Reserve have steadily implemented ASDRS over the past 3 years as a means to improve the historically abysmal dental readiness of the Army Reserve Component (RC). Dental readiness is essential for sustaining an Army RC Operational Force. ASDRS is a tool for RC commanders to provide contract dental readiness care in support of over 558 thousand nonmobilized Selected Reserve Citizen-Soldiers dispersed throughout the 54 states and U.S. territories, at home station before alert, and if necessary after alert (throughout the Army force generation cycle). This article examines the status of ASDRS implementation, assesses its effectiveness in improving Army RC Dental Readiness, and provides Army leadership recommendations regarding the following focus areas: (1) command emphasis, (2) program execution, and (3) synergy with the Military Health System and Department of Veterans Affairs.

  20. CBRecSys 2015. New Trends on Content-Based Recommender Systems

    DEFF Research Database (Denmark)

    While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in...