Reafee, Waleed; Salim, Naomie; Khan, Atif
The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy.
Zhang, Chuxu; Yu, Lu; Zhang, Xiangliang; Chawla, Nitesh
Data sparsity and cold-start problems are prevalent in recommender systems. To address such problems, both the observable explicit social information (e.g., user-user trust connections) and the inferable implicit correlations (e.g., implicit
Lu L.; Medo M.; Yeung C.H.; Zhang Y.-C.; Zhang Z.-K.; Zhou T.
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...
Kembellec, Gérald; Saleh, Imad
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
Zhang, Heng-Ru; Min, Fan; He, Xu; Xu, Yuan-Yuan
Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction...
Data sparsity and cold-start problems are prevalent in recommender systems. To address such problems, both the observable explicit social information (e.g., user-user trust connections) and the inferable implicit correlations (e.g., implicit neighbors computed by similarity measurement) have been introduced to complement user-item ratings data for improving the performances of traditional model-based recommendation algorithms such as matrix factorization. Although effective, (1) the utilization of the explicit user-user social relationships suffers from the weakness of unavailability in real systems such as Netflix or the issue of sparse observable content like 0.03% trust density in Epinions, thus there is no or little explicit social information that can be employed to improve baseline model in real applications; (2) the current similarity measurement approaches focus on inferring implicit correlations between a user (item) and their direct neighbors or top-k similar neighbors based on user-item ratings bipartite network, so that they fail to comprehensively unfold the indirect potential relationships among users and items. To solve these issues regarding both explicit/implicit social recommendation algorithms, we design a joint model of matrix factorization and implicit walk integrative learning, i.e., ImWalkMF, which only uses explicit ratings information yet models both direct rating feedbacks and multiple direct/indirect implicit correlations among users and items from a random walk perspective. We further propose a combined strategy for training two independent components in the proposed model based on sampling. The experimental results on two real-world sparse datasets demonstrate that ImWalkMF outperforms the traditional regularized/probabilistic matrix factorization models as well as other competitive baselines that utilize explicit/implicit social information.
Sifa, Rafet; Bauckhage, C.; Drachen, Anders
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....... 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...
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.
Ruiz-Montiel, Manuela; Aldana-Montes, José F.
Recommender Systems have become a significant area in the context of web personalization, given the large amount of available data. Ontologies can be widely taken advantage of in recommender systems, since they provide a means of classifying and discovering of new information about the items to recommend, about user profiles and even about their context. We have developed a semantically enhanced recommender system based on this kind of ontologies. In this paper we present a description of the proposed system.
Nürnberger, Miriam; Nerb, Josef; Schmitz, Florian; Keller, Johannes; Sütterlin, Stefan
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…
Manouselis, Nikos; Verbert, Katrien; Duval, Erik
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.
Jeckmans, Arjan; Beye, Michael; Erkin, Zekeriya; Erkin, Zekeriya; Hartel, Pieter H.; Lagendijk, Reginald; Tang, Qiang; Ramzan, Naeem; van Zwol, Roelof; Lee, Jong-Seok; Clüver, Kai; Hua, Xian-Sheng
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
Erkin, Zekeriya; Erkin, Zekeriya; Beye, Michael; Veugen, Thijs; Lagendijk, Reginald L.
Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we
de Graaff, V.; van Keulen, Maurice; de By, R.A.; de By, Rolf A.
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
Rizaldy Hafid Arigi, L.; Abdurahman Baizal, Z. K.; Herdiani, Anisa
Recommender System is software that is able to provide personalized recommendation suits users’ needs. Recommender System has been widely implemented in various domains, including tourism. One approach that can be done for more personalized recommendations is the use of contextual information. This paper proposes a context aware recommender based ontology system in the tourism domain. The system is capable of recommending tourist destinations by using user preferences of the categories of tourism and contextual information such as user locations, weather around tourist destinations and close time of destination. Based on the evaluation, the system has accuracy of of 0.94 (item recommendation precision evaluated by expert) and 0.58 (implicitly from system-end user interaction). Based on the evaluation of user satisfaction, the system provides a satisfaction level of more than 0.7 (scale 0 to 1) for speed factors for providing liked recommendations (PE), informative description of recommendations (INF) and user trust (TR).
With the amount of mobile applications available increasing rapidly, users have to put a lot of effort into finding applications of interest. The purpose of this thesis is to investigate how to aid users in the process of discovering new mobile applications by providing them with recommendations. A prototype system is then built as a proof-of-concept. The work of the thesis is divided into three phases where the aim of the first phase is to study related work and related systems to identify p...
Aldrich, Susan E.
Commercial recommender systems are deployed by marketing teams to increase revenue and/or personalize user experience. Marketers evaluate recommender systems not on its algorithms but on how well the vendor‘s expertise and interfaces will support achieving business goals. Driven by a business model that pays based on recommendation success, vendors guide clients through continuous optimization of recommendations. While recommender technology is mature, the solutions and market are still young...
Full Text Available This paper presents the classification process in a recommender system used for textual documents taken especially from web. The system uses in the classification process a combination of content filters, event filters and collaborative filters and it uses implicit and explicit feedback for evaluating documents.
Starns, Jeffrey J; Ratcliff, Roger; McKoon, Gail
It is currently controversial whether priming on implicit tasks and discrimination on explicit recognition tests are supported by a single memory system or by multiple, independent systems. In a Psychological Review article, Berry and colleagues used mathematical modeling to address this question and provide compelling evidence against the independent-systems approach. Copyright © 2012 Elsevier Ltd. All rights reserved.
Moreau, Luc; van der Schaft, Arjan
We introduce a class of optimal control problems on manifolds which gives rise (via the Pontryagin maximum principle) to a class of implicit Lagrangian systems (a notion which is introduced in the paper). We apply this to the mathematical modeling of interconnected mechanical systems and mechanical
Robillard, Martin P; Walker, Robert J; Zimmermann, Thomas
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...
Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang
An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.
Cleomar Valois Batista Jr
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.
Biljana PETREVSKA; Saso KOCESKI
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...
Sanchez-Bocanegra, C L; Sanchez-Laguna, F; Sevillano, J L
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.
Ren, Xiaolong; Lü, Linyuan; Liu, Runran; Zhang, Jianlin
Recommender systems use the historical activities and personal profiles of users to uncover their preferences and recommend objects. Most of the previous methods are based on objects’ (and/or users’) similarity rather than on their difference. Such approaches are subject to a high risk of increasingly exposing users to a narrowing band of popular objects. As a result, a few objects may be recommended to an enormous number of users, resulting in the problem of recommendation congestion, which is to be avoided, especially when the recommended objects are limited resources. In order to quantitatively measure a recommendation algorithm's ability to avoid congestion, we proposed a new metric inspired by the Gini index, which is used to measure the inequality of the individual wealth distribution in an economy. Besides this, a new recommendation method called directed weighted conduction (DWC) was developed by considering the heat conduction process on a user–object bipartite network with different thermal conductivities. Experimental results obtained for three benchmark data sets showed that the DWC algorithm can effectively avoid system congestion, and greatly improve the novelty and diversity, while retaining relatively high accuracy, in comparison with the state-of-the-art methods. (paper)
Full Text Available We present modular implicits, an extension to the OCaml language for ad-hoc polymorphism inspired by Scala implicits and modular type classes. Modular implicits are based on type-directed implicit module parameters, and elaborate straightforwardly into OCaml's first-class functors. Basing the design on OCaml's modules leads to a system that naturally supports many features from other languages with systematic ad-hoc overloading, including inheritance, instance constraints, constructor classes and associated types.
Jain, Rajshree; Tyagi, Jaya; Singh, Sandeep Kumar; Alam, Taj
Recommender systems and context awareness is currently a vital field of research. Most hybrid recommendation systems implement content based and collaborative filtering techniques whereas this work combines context and collaborative filtering. The paper presents a hybrid context aware recommender system for books and movies that gives recommendations based on the user context as well as user or item similarity. It also addresses the issue of dimensionality reduction using weighted pre filtering based on dynamically entered user context and preference of context. This unique step helps to reduce the size of dataset for collaborative filtering. Bias subtracted collaborative filtering is used so as to consider the relative rating of a particular user and not the absolute values. Cosine similarity is used as a metric to determine the similarity between users or items. The unknown ratings are calculated and evaluated using MSE (Mean Squared Error) in test and train datasets. The overall process of recommendation has helped to personalize recommendations and give more accurate results with reduced complexity in collaborative filtering.
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......) 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...... 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....
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.
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.
Guseva, A. I.; Kireev, V. S.; Bochkarev, P. V.; Kuznetsov, I. A.; Philippov, S. A.
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.
Winkels, R.; Boer, A.; Vredebregt, B.; van Someren, A.
In this paper we present the results of ongoing research aimed at a legal recommender system where users of a legislative portal receive suggestions of other relevant sources of law, given a focus document. We describe how we make references in case law to legislation explicit and machine readable,
Pedro, Luís; Santos, Carlos; Almeida, Sara Filipa; Ramos, Fernando; Moreira, António; Almeida, Margarida; Antunes, Maria João
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…
Lei, Fei; Du, Bin; Liu, Xin; Xie, Xiaoping; Chai, Tian
In this paper, implicit constrained multi-physics model of a motor wheel for an electric vehicle is built and then optimized. A novel optimization approach is proposed to solve the compliance problem between implicit constraints and stochastic global optimization. Firstly, multi-physics model of motor wheel is built from the theories of structural mechanics, electromagnetism and thermal physics. Then, implicit constraints are applied from the vehicle performances and magnetic characteristics. Implicit constrained optimization is carried out by a series of unconstrained optimization and verifications. In practice, sequentially updated subspaces are designed to completely substitute the original design space in local areas. In each subspace, a solution is obtained and is then verified by the implicit constraints. Optimal solutions which satisfy the implicit constraints are accepted as final candidates. The final global optimal solution is optimized from those candidates. Discussions are carried out to discover the differences between optimal solutions with unconstrained problem and different implicit constrained problems. Results show that the implicit constraints have significant influences on the optimal solution and the proposed approach is effective in finding the optimals. - Highlights: • An implicit constrained multi-physics model is built for sizing a motor wheel. • Vehicle dynamic performances are applied as implicit constraints for nonlinear system. • An efficient novel optimization is proposed to explore the constrained design space. • The motor wheel is optimized to achieve maximum efficiency on vehicle dynamics. • Influences of implicit constraints on vehicle performances are compared and analyzed.
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.
Stekh, Yu.; Artsibasov, V.
Modern Word Wide Web contains a large number of Web sites and pages in each Web site. Web recommendation system (recommendation system for web pages) are typically implemented on web servers and use the data obtained from the collection viewed web templates (implicit data) or user registration data (explicit data). In article considering methods and algorithms of web recommendation system based on the technology of data mining (web mining). Сучасна мережа Інтернет містить велику кількість веб...
Jensen, Christian D.; Pilkauskas, Povilas; Lefévre, Thomas
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...... an evaluation of four existing knowledge classification schemes with respect to these requirements. This evaluation helped us identify a classification scheme, which we have implemented in the current version of the Wikipedia Recommender System....... 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...
Jensen, Christian D.; Pilkauskas, Povilas; Lefevre, Thomas
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...... an evaluation of four existing knowledge classification schemes with respect to these requirements. This evaluation helped us identify a classification scheme, which we have implemented in the current version of the Wikipedia Recommender System....... 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...
Henneken, Edwin; Kurtz, Michael
A recommender system is a filtering algorithm that helps you find the right match by offering suggestions based on your choices and information you have provided. A latent factor model is a successful approach. Here an item is characterized by a vector describing to what extent a product is described by each of N categories, and a person is characterized by an ``interest'' vector, based on explicit or implicit feedback by this user. The recommender system assigns ratings to new items and suggests items this user might be interested in. Here we present results of a recommender system designed to find recent literature of interest to people working in the field of solid state physics. Since we do not have explicit feedback, our user vector consists of (implicit) ``usage.'' Using a system of N keywords we construct normalized keyword vectors for articles based on the keywords of that article and its bibliography. The normalized ``interest'' vector is created by calculating the normalized frequency of keyword occurrence in the papers cited by the papers read.
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.
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
A recommender system is a project that helps users identify their wishes and needs. The recommender system has been successfully applied to many e-commerce environments, such as news, film, music, books and other areas of recommendation. This paper mainly discusses the application of recommendation technology in software engineering, data and knowledge engineering, configurable projects and persuasion technology, and summarizes the development trend of recommendation technology in the future.
Manouselis, Nikos; Drachsler, Hendrik; Vuorikari, Riina; Hummel, Hans; Koper, Rob
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415). Berlin: Springer.
Knijnenburg, B.P.; Willemsen, M.C.; Gantner, Z.; Soncu, H.; Newell, C.
Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework
Dai, Wenlong; Woodward, P.R.
An iterative implicit-explicit hybrid scheme is proposed for hyperbolic systems of conservation laws. Each wave in a system may be implicitly, or explicitly, or partially implicitly and partially explicitly treated depending on its associated Courant number in each numerical cell, and the scheme is able to smoothly switch between implicit and explicit calculations. The scheme is of Godunov-type in both explicit and implicit regimes, is in a strict conservation form, and is accurate to second-order in both space and time for all Courant numbers. The computer code for the scheme is easy to vectorize. Multicolors proposed in this paper may reduce the number of iterations required to reach a converged solution by several orders for a large time step. The feature of the scheme is shown through numerical examples. 38 refs., 12 figs
Kantak, Shailesh S; Mummidisetty, Chaithanya K; Stinear, James W
Implicit and explicit memory systems for motor skills compete with each other during and after motor practice. Primary motor cortex (M1) is known to be engaged during implicit motor learning, while dorsal premotor cortex (PMd) is critical for explicit learning. To elucidate the neural substrates underlying the interaction between implicit and explicit memory systems, adults underwent a randomized crossover experiment of anodal transcranial direct current stimulation (AtDCS) applied over M1, PMd or sham stimulation during implicit motor sequence (serial reaction time task, SRTT) practice. We hypothesized that M1-AtDCS during practice will enhance online performance and offline learning of the implicit motor sequence. In contrast, we also hypothesized that PMd-AtDCS will attenuate performance and retention of the implicit motor sequence. Implicit sequence performance was assessed at baseline, at the end of acquisition (EoA), and 24 h after practice (retention test, RET). M1-AtDCS during practice significantly improved practice performance and supported offline stabilization compared with Sham tDCS. Performance change from EoA to RET revealed that PMd-AtDCS during practice attenuated offline stabilization compared with M1-AtDCS and sham stimulation. The results support the role of M1 in implementing online performance gains and offline stabilization for implicit motor sequence learning. In contrast, enhancing the activity within explicit motor memory network nodes such as the PMd during practice may be detrimental to offline stabilization of the learned implicit motor sequence. These results support the notion of competition between implicit and explicit motor memory systems and identify underlying neural substrates that are engaged in this competition. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.
Drachsler, Hendrik; Verbert, Katrien; Santos, Olga C.; Manouselis, Nikos
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
Koenig, Phyllis; Smith, Edward E; Troiani, Vanessa; Anderson, Chivon; Moore, Peachie; Grossman, Murray
We used a prototype extraction task to assess implicit learning of a meaningful novel visual category. Cortical activation was monitored in young adults with functional magnetic resonance imaging. We observed occipital deactivation at test consistent with perceptually based implicit learning, and lateral temporal cortex deactivation reflecting implicit acquisition of the category's semantic nature. Medial temporal lobe (MTL) activation during exposure and test suggested involvement of explicit memory as well. Behavioral performance of Alzheimer's disease (AD) patients and healthy seniors was also assessed, and AD performance was correlated with gray matter volume using voxel-based morphometry. AD patients showed learning, consistent with preserved implicit memory, and confirming that AD patients' implicit memory is not limited to abstract patterns. However, patients were somewhat impaired relative to healthy seniors. Occipital and lateral temporal cortical volume correlated with successful AD patient performance, and thus overlapped with young adults' areas of deactivation. Patients' severe MTL atrophy precluded involvement of this region. AD patients thus appear to engage a cortically based implicit memory mechanism, whereas their relative deficit on this task may reflect their MTL disease. These findings suggest that implicit and explicit memory systems collaborate in neurologically intact individuals performing an ostensibly implicit memory task.
Full Text Available In this paper, we propose a general framework for an intelligent recommender system that extends the concept of a knowledge-based recommender system. The intelligent recommender system exploits knowledge, learns, discovers new information, infers preferences and criticisms, among other things. For that, the framework of an intelligent recommender system is defined by the following components: knowledge representation paradigm, learning methods, and reasoning mechanisms. Additionally, it has five knowledge models about the different aspects that we can consider during a recommendation: users, items, domain, context and criticisms. The mix of the components exploits the knowledge, updates it and infers, among other things. In this work, we implement one intelligent recommender system based on this framework, using Fuzzy Cognitive Maps (FCMs. Next, we test the performance of the intelligent recommender system with specialized criteria linked to the utilization of the knowledge in order to test the versatility and performance of the framework.
Wei Liu; Linzhi Gao
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...
Drachsler, Hendrik; Verbert, Katrien; Santos, Olga; Manouselis, Nikos
This chapter presents an analysis of recommender systems in Technology-Enhanced 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 systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their c...
Barry, J.M.; Pollard, J.P.
An implicit iterative scheme for the solution of large systems of linear equations arising from neutron diffusion studies is presented. The method is applied to three-dimensional reactor studies and its performance is compared with alternative iterative approaches
Garcia, Inma; Sebastia, Laura; Onaindia, Eva; Guzman, Cesar
This paper introduces a method for giving recommendations of tourist activities to a group of users. This method makes recommendations based on the group tastes, their demographic classification and the places visited by the users in former trips. The group recommendation is computed from individual personal recommendations through the use of techniques such as aggregation, intersection or incremental intersection. This method is implemented as an extension of the e-Tourism tool, which is a user-adapted tourism and leisure application, whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven search engine that manages the group recommendation.
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.
Bogers, Toine; Koolen, Marijn; Musto, Cataldo
This article reports on the CBRecSys 2016 workshop, the third edition of the workshop on New Trends in Content-based Recommender Systems, co-located with RecSys 2016 in Boston, MA. Content-based recommendation has been applied successfully in many different domains, but it has not seen the same...... for work dedicated to all aspects of content-based recommender systems....... level of attention as collaborative filtering techniques have. Nevertheless, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. The CBRecSys workshop series provides a dedicated venue...
Jia Zhi Yang
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.
Omisore, M. O.; Samuel, O. W.
The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that…
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.
Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhou, Tao
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.
Manouselis, Nikos; Drachsler, Hendrik; Verbert, Katrien; Santos, Olga
Manouselis, N., Drachsler, H., Verbert, K., & Santos, C. S. (Eds.) (2010). Recommender System in Technology Enhanced Learning. Elsevier Procedia Computer Science: Volume 1, Issue 2. Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL). September, 29-30,
Ramli, Rindra M.
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
Chen, Mu-Yen; Wu, Ming-Ni; Chen, Chia-Chen; Chen, Young-Long; Lin, Hsien-En
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 Indoo...
Uzunoglu, B.; Hussaini, Y.
Implicit Particle Filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability by an implicit step . It optimizes a nonlinear cost function which can be inherited from legacy assimilation routines . Dynamic state estimation for almost real-time applications in power systems are becomingly increasingly more important with integration of variable wind and solar power generation. New advanced state estimation tools that will replace the old generation state estimation in addition to having a general framework of complexities should be able to address the legacy software and able to integrate the old software in a mathematical framework while allowing the power industry need for a cautious and evolutionary change in comparison to a complete revolutionary approach while addressing nonlinearity and non-normal behaviour. This work implements implicit particle filter as a state estimation tool for the estimation of the states of a power system and presents the first implicit particle filter application study on a power system state estimation. The implicit particle filter is introduced into power systems and the simulations are presented for a three-node benchmark power system . The performance of the filter on the presented problem is analyzed and the results are presented.
Pazos Arias, José J; Díaz Redondo, Rebeca P
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...
SNEHA KHATWANI; DR. M.B. CHANDAK
The contents of e-Commerce such as music, movies, books and electronics goods are necessary for a modern life style. But, it becomes difficult to find content according to users likes and users preference. An approach which produces desirable results to solve such the problem is to develop "Recommender System." The Recommender System of an e-Commerce site selects and suggests the contents to meet user's preference automatically using data sets of previous users stored in database. There ca...
Katarya, Rahul; Verma, Om Prakash
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.
Full Text Available To provide convenience for the user that frequently read the news, a system to gather, classify, and provide news from several news websites in one place was needed. This system utilized a recommender system to provide only relevant news to the user. This research proposed a system architecture that used vector space model, and Rocchio relevance feedback to provide specific news recommendation to user’s feedback. The results are that the proposed system architecture can achieve the goal by using five levels of feedback from the user. However, the time needed to gather news is increasing exponentially in line with the number of terms gathered from articles.
Lampropoulos, Aristomenis S
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 ...
This thesis investigates proactive recommender systems to avoid information overload inside a car. The proposed system delivers context-adaptive items in the right situation. Explicit explanations are used to make the system comprehensible because it works without user request. To show the applicability of our system, we investigate the acceptance of the drivers. The results show that the drivers tend to accept such a system. Zur Vermeidung von Informationsüberflutung im Fahrzeug werden in...
Puviani, Luca; Rama, Sidita
Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.
Suriati, S.; Dwiastuti, Meisyarah; Tulus, T.
Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering. Content-based filtering does not involve opinions from human to make the prediction, while collaborative filtering does, so collaborative filtering can predict more accurately. However, collaborative filtering cannot give prediction to items which have never been rated by any user. In order to cover the drawbacks of each approach with the advantages of other approach, both approaches can be combined with an approach known as hybrid technique. Hybrid technique used in this work is weighted technique in which the prediction score is combination linear of scores gained by techniques that are combined.The purpose of this work is to show how an approach of weighted hybrid technique combining content-based filtering and item-based collaborative filtering can work in a movie recommender system and to show the performance comparison when both approachare combined and when each approach works alone. There are three experiments done in this work, combining both techniques with different parameters. The result shows that the weighted hybrid technique that is done in this work does not really boost the performance up, but it helps to give prediction score for unrated movies that are impossible to be recommended by only using collaborative filtering.
Witteman, Holly; Chignell, Mark; Krahn, Murray
One of the challenges for people seeking health information online is the difficulty in locating health Websites that are personally relevant, credible and useful. We developed a Web-based recommender system in order to help address this problem in the context of prostate cancer. We are conducting an online randomized controlled trial to evaluate the accuracy of its recommendations and to compare the efficacy of content-based and collaborative filtering.
Veland, Oeystein; Kaarstad, Magnhild; Seim, Lars Aage; Foerdestroemmen, Nils T.
This document describes the result of a study on alarm systems conducted by IFE in Halden. The study was initiated by the Norwegian Petroleum Directorate. The objective was to identify and formulate a set of important properties for useful and usable alarm systems. The study is mainly based on review of the latest international recognised guidelines and standards on alarm systems available at the time of writing, with focus on realistic solutions from research and best practice from different industries. In addition, IFE experiences gathered through specification and design of alarm systems and experimental activities in HAMMLAB and bilateral projects, have been utilized where relevant. The document presents a total of 43 recommendations divided into a number of general recommendations and more detailed recommendations on alarm generation, structuring, prioritisation, presentation and handling. (Author)
Kye, Won-Ho, E-mail: email@example.com [Korean Intellectual Property Office, Government Complex Daejeon Building 4, 189, Cheongsa-ro, Seo-gu, Daejeon 302-701 (Korea, Republic of)
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.
Net Promoter System (NPS) is well known as an evaluation measure of the growth engine of big companies in the business area. The ultimate goal of my research is to build an action rules and meta-actions based recommender system for improving NPS scores of 34 companies (clients) dealing with similar businesses in the US and Canada. With the given…
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).
Full Text Available Our work is generally focused on recommending for small or medium-sized e-commerce portals, where explicit feedback is absent and thus the usage of implicit feedback is necessary. Nonetheless, for some implicit feedback features, the presentation context may be of high importance. In this paper, we present a model of relevant contextual features affecting user feedback, propose methods leveraging those features, publish a dataset of real e-commerce users containing multiple user feedback indicators as well as its context and finally present results of purchase prediction and recommendation experiments. Off-line experiments with real users of a Czech travel agency website corroborated the importance of leveraging presentation context in both purchase prediction and recommendation tasks.
Mabius, L.; Kaufman, H.
This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.
Full Text Available Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.
Ye, Yanming; Yin, Jianwei; Xu, Yueshen
Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.
Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.
In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.
Ramli, Rindra M.
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.
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......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...... implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. On the second part we describe the design of hybrid recommender ensemble (user, item and content based). The newly introduced algorithms...
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
Parra Santander, Denis Alejandro
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…
Fu, Shunkai; Zhang, Yao; Seinminn
Libraries are important to universities, and they have two primary features: readers as well as collections are highly professional. In this study, based on the experimental study with five millions of users' borrowing records, our discussion covers: (1) the necessity of recommender system for university libraries; (2) collaborative filtering (CF)…
With the explosive growth of the entertainment contents and the ubiquitous access of them via fixed or mobile computing devices, recommendation systems become essential tools to help the user to find the right entertainment at the right time and location. I envision that by integrating the bio
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…
Eberhardt, Katharina; Esser, Sarah; Haider, Hilde
According to the Theory of Event Coding (TEC; Hommel, Müsseler, Aschersleben, & Prinz, 2001), action and perception are represented in a shared format in the cognitive system by means of feature codes. In implicit sequence learning research, it is still common to make a conceptual difference between independent motor and perceptual sequences. This supposedly independent learning takes place in encapsulated modules (Keele, Ivry, Mayr, Hazeltine, & Heuer 2003) that process information along single dimensions. These dimensions have remained underspecified so far. It is especially not clear whether stimulus and response characteristics are processed in separate modules. Here, we suggest that feature dimensions as they are described in the TEC should be viewed as the basic content of modules of implicit learning. This means that the modules process all stimulus and response information related to certain feature dimensions of the perceptual environment. In 3 experiments, we investigated by means of a serial reaction time task the nature of the basic units of implicit learning. As a test case, we used stimulus location sequence learning. The results show that a stimulus location sequence and a response location sequence cannot be learned without interference (Experiment 2) unless one of the sequences can be coded via an alternative, nonspatial dimension (Experiment 3). These results support the notion that spatial location is one module of the implicit learning system and, consequently, that there are no separate processing units for stimulus versus response locations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Tekin, Cem; Zhang, Simpson; van der Schaar, Mihaela
In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the user. In contrast to centralized recommender systems, in which there is a single centralized seller who has access to the complete inventory of items as well as the complete record of sales and user information, in decentralized recommender systems each seller/learner only has access to the inventory of items and user information for its own products and not the products and user information of other sellers, but can get commission if it sells an item of another seller. Therefore the sellers must distributedly find out for an incoming user which items to recommend (from the set of own items or items of another seller), in order to maximize the revenue from own sales and commissions. We formulate this problem as a cooperative contextual bandit problem, analytically bound the performance of the sellers compared to the best recommendation strategy given the complete realization of user arrivals and the inventory of items, as well as the context-dependent purchase probabilities of each item, and verify our results via numerical examples on a distributed data set adapted based on Amazon data. We evaluate the dependence of the performance of a seller on the inventory of items the seller has, the number of connections it has with the other sellers, and the commissions which the seller gets by selling items of other sellers to its users.
Full Text Available Explaining interfaces become a useful tool in systems that have a lot of content to evaluate by users. The different interfaces represent a help for the undecided users or those who consider systems as boxed black smart. These systems present recommendations to users based on different learning models. In this paper, we present the different objectives of the explanation interfaces and some of the criteria that you can evaluate, as well as a proposal of metrics to obtain results in the experiments. Finally, we showed the main results of a study with real users and their interaction with e-commerce systems. Among the main results, highlight the positive impact in relation to the time of interaction with the applications and acceptance of the recommendations received.
Monteiro, Eriksson; Valente, Frederico; Costa, Carlos; Oliveira, José Luís
The large volume of data captured daily in healthcare institutions is opening new and great perspectives about the best ways to use it towards improving clinical practice. In this paper we present a context-based recommender system to support medical imaging diagnostic. The system relies on data mining and context-based retrieval techniques to automatically lookup for relevant information that may help physicians in the diagnostic decision.
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.
Knijnenburg, B.P.; Willemsen, M.C.; Hirtbach, S.; Buccafurri, F.; Semeraro, G.
This paper systematically evaluates the user experience of a recommender system. Using both behavioral data and subjective measures of user experience, we demonstrate that choice satisfaction and system effectiveness increase when a system provides personalized recommendations (compared to the same
The following Earth Observing Systems (E.O.S.) recommendations were suggested: (1) a program must be initiated to ensure that present time series of Earth science data are maintained and continued. (2) A data system that provides easy, integrated, and complete access to past, present, and future data must be developed as soon as possible. (3) A long term research effort must be sustained to study and understand these time series of Earth observations. (4) The E.O.S. should be established as an information system to carry out those aspects of the above recommendations which go beyond existing and currently planned activities. (5) The scientific direction of the E.O.S. should be established and continued through an international scientific steering committee.
Bogers, Toine; Koolen, Marijn
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...... venue for work dedicated to all aspects of content-based recommender systems....... the same level of attention as collaborative filtering techniques have. Nevertheless, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. The CBRecSys workshop series provides a dedicated...
Rustam Vahidov; Raafat Saade; Ahmed Eldiwany
Internet allows investors to use friendly tools which help them to make and implement their investment choices in an online environment. Individuals can have access to volumes of information related to alternative financial instruments and craft their strategies according to their needs and preferences. However, in the presence of multiple choices, investors with limited experience and knowledge may need support in making adequate decisions. Recommendations systems have been used in e-commerc...
With the explosively growing of the technologies and services of the Internet, the information data world increases rapidly. Recommendation systems have acted an important role in many ways, including movies, books, friends, shopping on net and music. Especially like today, people are surrounded by mass of information. People try to find their preferred movies. It has become difficult when facing so many kinds of movies. People may dizzied by plenty of items on the net, they don't know how to...
Full Text Available In recommender systems (RS, many models are designed to predict ratings of items for the target user. To improve the performance for rating prediction, some studies have introduced tags into recommender systems. Tags benefit RS considerably, however, they are also redundant and ambiguous. In this paper, we propose a hybrid deep learning model TRSDL (tag-aware recommender system based on deep learning to improve the performance of tag-aware recommender systems (TRS. First, TRSDL uses pre-trained word embeddings to represent user-defined tags, and constructs item and user profiles based on the items’ tags set and users’ tagging behaviors. Then, it utilizes deep neural networks (DNNs and recurrent neural networks (RNNs to extract the latent features of items and users, respectively. Finally, it predicts ratings from these latent features. The model not only addresses tag limitations and takes advantage of semantic tag information but also learns more advanced implicit features via deep structures. We evaluated our proposed approach and several baselines on MovieLens-20 m, and the experimental results demonstrate that TRSDL significantly outperforms all the baselines (including the state-of-the-art models BiasedMF and I-AutoRec. In addition, we also explore the impacts of network depth and type on model performance.
Sheth, Swapneel; Arora, Nipun; Murphy, Christian; Kaiser, Gail
Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively little research, however, in the area of generalized system architectures for recommendation systems. In this paper, we introduce weHelp : a reference architecture for social recommender systems - systems where recommendations are derived automatically from the aggregate of logged activities conducted by the system's users. Our architecture is designed to be application and domain agnostic. We feel that a good reference architecture will make designing a recommendation system easier; in particular, weHelp aims to provide a practical design template to help developers design their own well-modularized systems.
Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther
Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.
Bernard Shibwabo Kasamani
Full Text Available This paper presents an implementation of recommender technology to online search of rental properties. In particular, the paper uses the preference-based search approach combined with a technique called example-critiquing. Rather than perform a query against the database, this approach prompts the user to express some preferences on rental properties, uses them to construct a preference model for the user, and finally generates a list of properties that best match that preferences. The system is developed as Web application using the Ruby on Rails framework
Ramli, Rindra M.
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).
With the arrival of the big data era, recommendation system has been a hot technology for enterprises to streamline their sales. Recommendation algorithms for individual users have been extensively studied over the past decade. Most existing recommendation systems also focus on individual user recommendations, however in many daily activities, items are recommended to the groups not one person. As an effective means to solve the problem of group recommendation problem,we extend the single use...
Recommender systems have been widely applied to assist user's decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user's tastes on the items may vary from contexts to contexts. Several context-aware recommendation algorithms have been proposed and developed to improve the quality of recommendations. However, there are limited research which...
Berg, Kristin Nergaard
It is known in the industry that occasional leakages occur in subsea production systems. In spite of efforts to apply subsea leak detectors, the experience is that most leakages are either detected by ROV during routine inspections or interventions or as oil slicks on the surface . Operators and authority awareness towards the environmental impact of oil and gas production is increasing. The regulatory bodies in Norway, EU and USA specify requirements for detection of acute pollution. This paper presents the development of a Recommended Practice (RP) sponsored by OLF (The Norwegian Oil Industry Association). The JIP includes several major oil and gas operators. The objective of the RP is to serve as a technical and practical reference in the field of subsea leak detection. (Author)
Full Text Available Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.
Wang, Yibo; Wang, Mingming; Xu, Wei
Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the propo...
Lu, Wei; Chung, Fu-Lai; Lai, Kunfeng; Zhang, Liang
Guessing what user may like is now a typical interface for video recommendation. Nowadays, the highly popular user generated content sites provide various sources of information such as tags for recommendation tasks. Motivated by a real world online video recommendation problem, this work targets at the long tail phenomena of user behavior and the sparsity of item features. A personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS) is hence proposed. Assuming that each clicking sample is generated from a representation of user preferences, DPIS models the sample level topic proportions as a multinomial item vector, and utilizes topical clustering on the user part for recommendation through a probit classifier. As demonstrated by the real-world application, the proposed DPIS achieves better performance in accuracy, perplexity as well as diversity in coverage than traditional methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
Implicit time integration methods have been used extensively in numerical modelling of slowly varying phenomena in systems that also support rapid variation. Examples include diffusion, hydrodynamics and reaction kinetics. This article discussed implementation of implicit time integration in plasma codes of the ''particle-in-cell'' family, and the benefits to be gained
Gräßer, Felix; Beckert, Stefanie; Küster, Denise; Schmitt, Jochen; Abraham, Susanne; Malberg, Hagen; Zaunseder, Sebastian
We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender , are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.
Full Text Available We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.
Erdt, Mojisola; Fernandez, Alejandro; Rensing, Christoph
The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like…
JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population
Suka Parwita, Wayan Gede; Winarko, Edi
Abstrak Recommendation 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 identi...
Chen, Shanshin; Tortorelli, Daniel A.; Hansen, John Michael
of ordinary diffferential equations is employed to avoid the instabilities associated with the direct integrations of differential-algebraic equations. To extend the unconditional stability of the implicit Newmark method to nonlinear dynamic systems, a discrete energy balance is enforced. This constraint......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...... the kinmatics of the system, and a choice of time integrations algorithms. The current approach uses a non-dissipative implict Newmark method to integrate the equations of motion defined in terms of the independent joint coordinates of the system. The reduction of the equations of motion to a minimal set...
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.
Hykes, J. M.; Ferrer, R. M.
The Bateman equations, which describe the transmutation of nuclides over time as a result of radioactive decay, absorption, and fission, are often numerically stiff. This is especially true if short-lived nuclides are included in the system. This paper describes the use of implicit numerical methods for o D Es applied to the stiff Bateman equations, specifically employing the Backward Differentiation Formulas (BDF) form of the linear multistep method. As is true in other domains, using an implicit method removes or lessens the (sometimes severe) step-length constraints by which explicit methods must abide. To gauge its accuracy and speed, the BDF method is compared to a variety of other solution methods, including Runge-Kutta explicit methods and matrix exponential methods such as the Chebyshev Rational Approximation Method (CRAM). A preliminary test case was chosen as representative of a PWR lattice depletion step and was solved with numerical libraries called from a Python front-end. The Figure of Merit (a combined measure of accuracy and efficiency) for the BDF method was nearly identical to that for CRAM, while explicit methods and other matrix exponential approximations trailed behind. The test case includes 319 nuclides, in which the shortest-lived nuclide is 98 Nb with a half-life of 2.86 seconds. Finally, the BDF and CRAM methods were compared within CASMO5, where CRAM had a FOM about four times better than BDF, although the BDF implementation was not fully optimized. (authors)
Merritt, Stephanie M; Heimbaugh, Heather; LaChapell, Jennifer; Lee, Deborah
This study is the first to examine the influence of implicit attitudes toward automation on users' trust in automation. Past empirical work has examined explicit (conscious) influences on user level of trust in automation but has not yet measured implicit influences. We examine concurrent effects of explicit propensity to trust machines and implicit attitudes toward automation on trust in an automated system. We examine differential impacts of each under varying automation performance conditions (clearly good, ambiguous, clearly poor). Participants completed both a self-report measure of propensity to trust and an Implicit Association Test measuring implicit attitude toward automation, then performed an X-ray screening task. Automation performance was manipulated within-subjects by varying the number and obviousness of errors. Explicit propensity to trust and implicit attitude toward automation did not significantly correlate. When the automation's performance was ambiguous, implicit attitude significantly affected automation trust, and its relationship with propensity to trust was additive: Increments in either were related to increases in trust. When errors were obvious, a significant interaction between the implicit and explicit measures was found, with those high in both having higher trust. Implicit attitudes have important implications for automation trust. Users may not be able to accurately report why they experience a given level of trust. To understand why users trust or fail to trust automation, measurements of implicit and explicit predictors may be necessary. Furthermore, implicit attitude toward automation might be used as a lever to effectively calibrate trust.
Recommender systems aim to support users in their decision-making process while interacting with large information spaces and recommend items of interest to users based on preferences they have expressed, either explicitly or implicitly. Recommender systems are increasingly used with product and service selection over the Internet. Although…
Full Text Available Currently, recommender systems (RS are incorporating implicit information from social circle of the Internet. The implicit social information in human mind is not easy to reflect in appropriate decision making techniques. This paper consists of 2 contributions. First, we develop an item-based prototype classifier (IPC in which a prototype represents a social circlers preferences as a pattern classification technique. We assume the social circle which distinguishes with others by the items their members like. The prototype structure of the classifier is defined by two2-dimensional matrices. We use information gain and OWA aggregator to construct a feature space. The item-based classifier assigns a new item to some prototypes with different prototypicalities. We reform a typical data setmIris data set in UCI Machine Learning Repository to verify our fuzzy prototype classifier. The second proposition of this paper is to give the application of IPC in recommender system to solve new item cold-start problems. We modify the dataset of MovieLens to perform experimental demonstrations of the proposed ideas.
The paper proposes adaptive web recommendation system based on user behavior. The proposed system uses expert system to evaluating and recommending suitable items of content. Relevant items are subsequently evaluated and filtered based on history of visited items and user´s preferred categories of items. Main parts of the proposed system are presented and described. The proposed recommendation system is verified on specific example.
Manouselis, Nikos; Verbert, Katrien
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
Drachsler, Hendrik; Hummel, Hans; Van den Berg, Bert; Eshuis, Jannes; Berlanga, Adriana; Nadolski, Rob; Waterink, Wim; Boers, Nanda; Koper, Rob
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.
Shanks, David R; Berry, Christopher J
This article reviews recent work aimed at developing a new framework, based on signal detection theory, for understanding the relationship between explicit (e.g., recognition) and implicit (e.g., priming) memory. Within this framework, different assumptions about sources of memorial evidence can be framed. Application to experimental results provides robust evidence for a single-system model in preference to multiple-systems models. This evidence comes from several sources including studies of the effects of amnesia and ageing on explicit and implicit memory. The framework allows a range of concepts in current memory research, such as familiarity, recollection, fluency, and source memory, to be linked to implicit memory. More generally, this work emphasizes the value of modern computational modelling techniques in the study of learning and memory.
Yeung, Chi Ho
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.
Zeng, An; Yeung, Chi Ho; Medo, Matúš; Zhang, Yi-Cheng
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.
Hernández de la Iglesia, Daniel; Moreno García, María N.; Omatu, Sigeru
Today information is a very important asset for organizations. Obtaining and interpreting information in real time can be a major benefit in decision making. For this reason, we have developed a mobile application that uses WiFi access points to locate users who are attending conferences and exhibits. The application can generate a dataset according to the user’s preferences and on-site location, and provide personalized recommendations accordingly.
Full Text Available While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.
Baraglia, Ranieri; Frattari, Claudio; Muntean, Cristina Ioana; Nardini, Franco Maria; Silvestri, Fabrizio
Recommendation systems provide focused information to users on a set of objects belonging to a specific domain. The proposed recommender system provides personalized suggestions about touristic points of interest. The system generates recommendations, consisting of touristic places, according to the current position of a tourist and previously collected data describing tourist movements in a touristic location/city. The touristic sites correspond to a set of points of interest identified a pr...
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.
Full Text Available One of the most crucial issues, nowadays, is to provide personalized services to each individual based on their preferences. To achieve this goal, recommender system could be utilized as a tool to help the users in decision-making process offering different items and options. They are utilized to predict and recommend relevant items to end users. In this case an item could be anything such as a document, a location, a movie, an article or even a user (friend suggestion. The main objective of the recommender systems is to suggest items which have great potential to be liked by users. In modern recommender systems, various methods are combined together with the aim of extracting patterns in available datasets. Combination of different algorithms make prediction more convoluted since various parameters should be taken into account in providing recommendations. Recommendations could be personalized or non-personalized. In non-personalized type, selection of the items for a user is based on the number of the times that an item has been visited in the past by other users. However, in the personalized type, the main objective is to provide the best items to the user based on her taste and preferences. Although, in many domains recommender systems gained significant improvements and provide better services for users, it still requires further research to improve accuracy of recommendations in many aspects. In fact, the current available recommender systems are far from the ideal model of the recommender system. This paper reviews state of art in recommender systems algorithms and techniques which is necessary to identify the gaps and improvement areas. In addition to that, we provide possible solutions to overcome shortages and known issues of recommender systems as well as discussing about recommender systems evaluation methods and metrics in details.
Dean-Hall, A.; Clarke, C.L.A.; Kamps, J.; Kiseleva, J.
In this work we describe a system to evaluate multiple point- of-interest recommendation systems. In this system each recommendation service will be exposed online and crowd-sourced assessors will interact with merged results from multiple services, which are responding to suggestion requests live,
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…
Full Text Available Recommender systems are information filtering tools that aspire to predict the rating for users and items, predominantly from big data to recommend their likes. Movie recommendation systems provide a mechanism to assist users in classifying users with similar interests. This makes recommender systems essentially a central part of websites and e-commerce applications. This article focuses on the movie recommendation systems whose primary objective is to suggest a recommender system through data clustering and computational intelligence. In this research article, a novel recommender system has been discussed which makes use of k-means clustering by adopting cuckoo search optimization algorithm applied on the Movielens dataset. Our approach has been explained systematically, and the subsequent results have been discussed. It is also compared with existing approaches, and the results have been analyzed and interpreted. Evaluation metrics such as mean absolute error (MAE, standard deviation (SD, root mean square error (RMSE and t-value for the movie recommender system delivers better results as our approach offers lesser value of the mean absolute error, standard deviation, and root mean square error. The experiment results obtained on Movielens dataset stipulate that the proposed approach may provide high performance regarding reliability, efficiency and delivers accurate personalized movie recommendations when compared with existing methods. Our proposed system (K-mean Cuckoo has 0.68 MAE, which is superior to existing work (0.78 MAE  and also has improvement of our previous work (0.75 MAE .
Zhou, Lei; El Helou, Sandy; Moccozet, Laurent; Opprecht, Laurent; Benkacem, Omar; Salzmann, Christophe; Gillet, Denis
From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different online learning platforms and delivers personalized recommendation. The underlying educational objective is to enable academic institutions to provide a Web 2.0 dashboard bringing together open resources from the Cloud and proprietary content...
Achin Jain; Vanita Jain; Nidhi Kapoor
Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are...
Hou, Lei; Liu, Kecheng; Liu, Jianguo; Zhang, Runtong
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.
Parr, V.B.; McCullough, L.D.; Tashjian, B.M.; Shirley, R.D.
A Decentralized Data Systems (DDS) is defined as a utility, industry, or regulatory agency data system dealing with power plant performance. DDSs in use or planned for use on an industry, regional, or utility basis have not been studied in sufficient detail to identify methods of coordinating them with the Information System for Generation Availability (ISGA), which is under development. A survey of utility, industry, and regulatory agency DDSs was made by Southwest Research Institute. Information was gathered on twelve utility data systems, two industry data systems, two regulatory agency data systems and one government-owned utility data system. The objectives of this study are to identify existing DDSs that are potential candidates for integration into the ISGA, and to identify methods by which that integration can be accomplished. A matrix of the data elements and formats was prepared for the data systems, which allowed comparison to determine which DDSs were potential candidates for integration into the ISGA. Utility data systems emphasize outage data collection. A composite of GADS, NPRDS, and piece-part data from Utah Power and Light encompass nearly all data elements identified in the survey. Of the computer system configurations considered as potentially viable for ISGA, integrated, centralized, interfaced, and distributed, the authors believe the centralized system for data retrieval is the least expensive to implement, and the most acceptable to the users. An in-depth study of ISGA hadware/software options is the subject of another EPRI contract. The information and system configuration overviews presented in this report will support that effort
Regarding to the increase in the online social networks services during the recent years, the recommender system has turned into an emerging research subject. Currently, regarding to the fast and consistent expansion of using the internet, the necessity of a recommender system for refining the large volume of data has ...
Ghauth, Khairil Imran; Abdullah, Nor Aniza
A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering).…
MCarthur, Stephen; Chen, Minjiang; Marinelli, Mattia
Deliverable 8.3 reports on the consolidation of experiences from visualisation, decision support prototypes experiments and recommendations on future developments of decision support systems......Deliverable 8.3 reports on the consolidation of experiences from visualisation, decision support prototypes experiments and recommendations on future developments of decision support systems...
Fazeli, Soude; Loni, Babak; Drachsler, Hendrik; Sloep, Peter
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
Fazeli, Soude; Loni, Babak; Drachsler, Hendrik; Sloep, Peter
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
Full Text Available Multi-reservoir systems management is complex because of the uncertainty on future events and the variety of purposes, usually conflicting, of the involved actors. An efficient management of these systems can help improving resource allocation, preventing political crisis and reducing the conflicts between the stakeholders. Bellman stochastic dynamic programming (SDP is the most famous among the many proposed approaches to solve this optimal control problem. Unfortunately, SDP is affected by the curse of dimensionality: computational effort increases exponentially with the complexity of the considered system (i.e., number of reservoirs, and the problem rapidly becomes intractable. This paper proposes an implicit stochastic optimization approach for the solution of the reservoir management problem. The core idea is using extremely flexible functions, such as artificial neural networks (NN, for designing release rules which approximate the optimal policies obtained by an open-loop approach. These trained NNs can then be used to take decisions in real time. The approach thus requires a sufficiently long series of historical or synthetic inflows, and the definition of a compromise solution to be approximated. This work analyzes with particular emphasis the importance of the information which represents the input of the control laws, investigating the effects of different degrees of completeness. The methodology is applied to the Nile River basin considering the main management objectives (minimization of the irrigation water deficit and maximization of the hydropower production, but can be easily adopted also in other cases.
Cavaglieri, Daniele; Bewley, Thomas
Implicit/explicit (IMEX) Runge-Kutta (RK) schemes are effective for time-marching ODE systems with both stiff and nonstiff terms on the RHS; such schemes implement an (often A-stable or better) implicit RK scheme for the stiff part of the ODE, which is often linear, and, simultaneously, a (more convenient) explicit RK scheme for the nonstiff part of the ODE, which is often nonlinear. Low-storage RK schemes are especially effective for time-marching high-dimensional ODE discretizations of PDE systems on modern (cache-based) computational hardware, in which memory management is often the most significant computational bottleneck. In this paper, we develop and characterize eight new low-storage implicit/explicit RK schemes which have higher accuracy and better stability properties than the only low-storage implicit/explicit RK scheme available previously, the venerable second-order Crank-Nicolson/Runge-Kutta-Wray (CN/RKW3) algorithm that has dominated the DNS/LES literature for the last 25 years, while requiring similar storage (two, three, or four registers of length N) and comparable floating-point operations per timestep.
Bhatti, Uzair Aslam; Huang, Mengxing; Wang, Hao; Zhang, Yu; Mehmood, Anum; Di, Wu
Immunization averts an expected 2 to 3 million deaths every year from diphtheria, tetanus, pertussis (whooping cough), and measles; however, an additional 1.5 million deaths could be avoided if vaccination coverage was improved worldwide. 1 1 Data source for immunization records of 1.5 M: http://www.who.int/mediacentre/factsheets/fs378/en/ New vaccination technologies provide earlier diagnoses, personalized treatments and a wide range of other benefits for both patients and health care professionals. Childhood diseases that were commonplace less than a generation ago have become rare because of vaccines. However, 100% vaccination coverage is still the target to avoid further mortality. Governments have launched special campaigns to create an awareness of vaccination. In this paper, we have focused on data mining algorithms for big data using a collaborative approach for vaccination datasets to resolve problems with planning vaccinations in children, stocking vaccines, and tracking and monitoring non-vaccinated children appropriately. Geographical mapping of vaccination records helps to tackle red zone areas, where vaccination rates are poor, while green zone areas, where vaccination rates are good, can be monitored to enable health care staff to plan the administration of vaccines. Our recommendation algorithm assists in these processes by using deep data mining and by accessing records of other hospitals to highlight locations with lower rates of vaccination. The overall performance of the model is good. The model has been implemented in hospitals to control vaccination across the coverage area.
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
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.
Full Text Available With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer’s feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.
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.
Marcos Santamarta, Victor
Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user?s preferences. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender sy...
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.
Elmisery, Ahmed M.
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.
B. A. Gobin; R. K. Subramanian
Knowledge modelling, a main activity for the development of Knowledge Based Systems, have no set standards and are mostly done in an ad hoc way. There is a lack of support for the transition from abstract level to implementation. In this paper, a methodology for the development of the knowledge model, which is inspired by both Software and Knowledge Engineering, is proposed. Use of UML which is the de-facto standard for modelling in the software engineering arena is explored for knowledge mod...
Christiansen, René Boyer; Gynther, Karsten; Petersen, Anne Kristine
The paper explores a shift in education from educational systems requiring student adaptation to educational recommendation systems adapting to students’ individual needs. The paper discusses the concept of adaptation as addressed in educational research and draws on the system theory of Heinz von...... Foerster to shed light on how the educational system has used and understood adaptation. In this context, we point out two different approaches to educational adaptation: 1) students adapting to the educational system and 2) the attempt of the educational system to adapt to students through automatized...... system adaptation and recommendation systems. These different understandings constitute a design framework that is used to analyze two current trends: Adaptive learning systems and learning analytics. Finally, the paper discusses the potential of looking at adaptation as recommendation systems...
Christiansen, René Boyer; Gynther, Karsten; Petersen, Anne Kristine
The paper explores a shift in education from educational systems requiring student adaptation to educational recommendation systems adapting to students’ individual needs. The paper discusses the concept of adaptation as addressed in educational research and draws on the system theory of Heinz von...... system adaptation and recommendation systems. These different understandings constitute a design framework that is used to analyze two current trends: Adaptive learning systems and learning analytics. Finally, the paper discusses the potential of looking at adaptation as recommendation systems...... Foerster to shed light on how the educational system has used and understood adaptation. In this context, we point out two different approaches to educational adaptation: 1) students adapting to the educational system and 2) the attempt of the educational system to adapt to students through automatized...
Erkin, Zekeriya; Erkin, Z.; Beye, M.; Veugen, T.; Lagendijk, R.L.
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
Winoto, Pinata; Tang, Tiffany Ya; McCalla, Gordon
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…
Zhang, Chuxu; Yu, Lu; Wang, Yan; Shah, Chirag; Zhang, Xiangliang
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
Magoulas, G.D.; van Setten, Mark; Veenstra, Mettina; Chen, S.Y; Nijholt, Antinus; van Dijk, Elisabeth M.A.G.
Recommender systems help people to find information that is interesting to them. However, current recommendation techniques only address the user's short-term and long-term interests, not their immediate interests. This paper describes a method to structure information (with or without using
Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min
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.
Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min
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
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.
Full Text Available Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than presenting articles in order of their occurrence. We present our research on developing personalized news recommendation system with the help of a popular micro-blogging service, “Twitter.” News articles are ranked based on the popularity of the article identified from Twitter’s public timeline. In addition, users construct profiles based on their interests and news articles are also ranked based on their match to the user profile. By integrating these two approaches, we present a hybrid news recommendation model that recommends interesting news articles to the user based on their popularity as well as their relevance to the user profile.
Gao, Baozhong; Du, Shouyan; Li, Xinzhi; Liu, Fangai
Currently, there still exists a host of problems in the book recommendation system, such as low accuracy, weak correlation and poor pertinence. Aiming to unravel these problems, this paper based on the theory of big data and data mining technology, through analyzing internet user behavior and the “5C” model of personal credit evaluation, combined with joint impact weight calculation method, which involves user grade, borrowing credit, book friend recommendation degree, book friend recommended adoption degree, borrowing frequency, borrowing number, and borrowing time interval. User activity and credit are also taken into account in the process of establishing user tagging system so as to build classified book recommendation service. This method is of universal meaning to the book recommendation service of smart campus with user as the core under big data environment.
Girelli, Luisa; Semenza, Carlo; Delazer, Margarete
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.
proposed in order to overcome the traditional problems of CRS. ... known method for matrix factorization that provides the lowest rank ..... Adomavicius G, Tuzhilin A 2005 Toward the next generation of recommender systems: A survey of.
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
Fazeli, Soude; Loni, Babak; Drachsler, Hendrik; Sloep, Peter
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 learning resources. We propose to make use of graph-walking methods for improving performance of the well-known baseline algorithms. We evaluate the proposed graph-based approach in terms of their ...
Gustavo Nogueira Guedes Pereira Rosa
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.
Bouneffouf , Djallel
The vast amount of information generated and maintained everyday by information systems and their users leads to the increasingly important concern of overload information. In this context, traditional recommender systems provide relevant information to the users. Nevertheless, with the recent dissemination of mobile devices (smartphones and tablets), there is a gradual user migration to the use of pervasive computing environments. The problem with the traditional recommendation approaches is...
A. Said (Alan); A. Bellogín Kouki (Alejandro); A.P. de Vries (Arjen)
htmlabstractThe 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
Roediger, H L
Explicit measures of human memory, such as recall or recognition, reflect conscious recollection of the past. Implicit tests of retention measure transfer (or priming) from past experience on tasks that do not require conscious recollection of recent experiences for their performance. The article reviews research on the relation between explicit and implicit memory. The evidence points to substantial differences between standard explicit and implicit tests, because many variables create dissociations between these tests. For example, although pictures are remembered better than words on explicit tests, words produce more priming than do pictures on several implicit tests. These dissociations may implicate different memory systems that subserve distinct memorial functions, but the present argument is that many dissociations can be understood by appealing to general principles that apply to both explicit and implicit tests. Phenomena studied under the rubric of implicit memory may have important implications in many other fields, including social cognition, problem solving, and cognitive development.
Deng, Shuiguang; Wang, Dongjing; Li, Ying; Cao, Bin; Yin, Jianwei; Wu, Zhaohui; Zhou, Mengchu
This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.
Peiris, K. Dharini Amitha; Gallupe, R. Brent
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…
Petersen, Anne Kristine; Christiansen, Rene B.; Gynther, Karsten
The paper explores a shift in education from educational systems requiring student adaptation to educational recommendation systems adapting to students' individual needs. The paper discusses the concept of adaptation as addressed in educational research and draws on the system theory of Heinz von Foerster to shed light on how the educational…
Liang, Yueling; Nie, Guihua
Current electronic commerce recommendation system is designed for single electronic commerce website and current recommendation technologies have obvious deficiencies Centralized recommendation systems can not resolve the contradiction between high recommendation quality and timely response, as well as that between limited recommendation range and ever rich information on the web. Distributed recommendation systems are expected to improve the recommendation quality while maintaining high perf...
Jiang, Na; Tan, Chee-Wee; Wang, Weiquan
Context-Aware Recommender Systems (CARSs) are becoming commonplace. Yet, there is a paucity of studies that investigates how such systems could affect usage behavior from a user-system interaction perspective. Building on the Social Interdependence Theory (SIT), we construct a research model...... of users’ promotive interaction with CARSs, which in turn, dictates the performance of such recommender systems. Furthermore, we introduce scrutability features as design interventions that can be harnessed by developers to mitigate the impact of users’ promotive interaction on the performance of CARSs....
José Aridiano Lima de Deus
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.
Haruna, Khalid; Akmar Ismail, Maizatul; Damiasih, Damiasih; Sutopo, Joko; Herawan, Tutut
Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.
The 2607-W6 septic system is not approved by the Washington State Department of Health. The system is over 40 years old and is operating at greater than 200% capacity. Under these conditions the system is subject to imminent failure and is not adequately treating the septic waste. This poses a potential personnel health risk. It is recommended that this system be upgraded by installation of a new drain field similar to the modification of the 2607-W1 system
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.
Full Text Available With the development of social networks and online mobile communities, group recommendation systems support users’ interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users’ personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external social-trust networks is proposed, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members inside and outside of the group. We employ the users’ degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.
Durao, Frederico; Dolog, Peter
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......, the proposal extends a hybrid tag-based recommender system with a semantic factor, which looks for tag relations in different semantic sources. In order to evaluate the benefits acquired with the semantic extension, we have compared the new findings with results from a previous experiment involving 38 people......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. This paper proposes a semantic...
Álvarez Márquez , Jesús ,; Ziegler , Jurgen
International audience; We present a novel approach to group recommender systems that better takes into account the social interaction in a group when formulating, discussing and negotiating the features of the item to be jointly selected. Our approach provides discussion support in a collaborative preference elicitation and negotiation process. Individual preferences are continuously aggregated and immediate feedback of the resulting recommendations is provided. We also support the last stag...
monitoring software is a java based program sending updates to the database on the sensor machine. The host monitoring program gathers information about...3.2.2 Database. A MySQL database located on the sensor machine acts as the storage for the sensors on the network. Snort, Nmap, vulnerability scores, and...machine with the IDS and the recommender is labeled “sensor”. The recommender system code is written in java and compiled using java version 1.6.024
Chen, Chih-Han; Karvela, Maria; Sohbati, Mohammadreza; Shinawatra, Thaksin; Toumazou, Christofer
The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.
.... A formal computational model for implicit invocation is presented. We develop a verification framework for implicit invocation that is based on Jones' rely/guarantee reasoning for concurrent systems Jon83,St(phi)91...
Erkin, Z.; Veugen, T.; Lagendijk, R.L.
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.
M. Clements (Maarten); A.P. de Vries (Arjen); J.A. Pouwelse; J. Wang (Jun); M.J.T. Reinders
textabstractRecommendation 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
Full Text Available Recommender systems are applications that have emerged in the e-commerce area in order to assist users in their searches in electronic shops. These shops usually offer a wide range of items that cover the necessities of a great variety of users. Nevertheless, searching in such a wide range of items could be a very difficult and time-consuming task. Recommender systems assist users to find out suitable items by means of recommendations based on information provided by different sources such as: other users, experts, item features, etc. Most of the recommender systems force users to provide their preferences or necessities using an unique numerical scale of information fixed in advance. In spite of this information is usually related to opinions, tastes and perceptions, therefore, it seems that is usually better expressed in a qualitative way, with linguistic terms, than in a quantitative way, with precise numbers. We propose a Knowledge Based Recommender System that uses the fuzzy linguistic approach to define a flexible framework to capture the uncertainty of the user's preferences. Thus, this framework will allow users to express their necessities in scales closer to their own knowledge, and different from the scale utilized to describe the items.
Zhou, Tao; Kuscsik, Zoltán; Liu, Jian-Guo; Medo, Matús; Wakeling, Joseph Rushton; Zhang, Yi-Cheng
Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.
Mei, Jing; Liu, Haifeng; Li, Xiang; Xie, Guotong; Yu, Yiqin
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.
Zou, Haitao; Gong, Zhiguo; Zhang, Nan; Zhao, Wei; Guo, Jingzhi
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.
Durao, Frederico; Dolog, Peter
-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......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...
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.
Yin, Xin; Song, Jinjie
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.
Wiesner, Martin; Pfeifer, Daniel
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.
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.
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.
Full Text Available Recommender systems are in widespread use in many areas, especially electronic commerce solutions. In this contribution, we apply recommender functionalities to business process modeling and investigate their potential for supporting process modeling. To do so, we have implemented two prototypes, demonstrated them at a major fair and collected user feedback. After analysis of the feedback, we have confronted the findings with the results of the experiment. Our results indicate that fairgoers expect increased modeling speed as the key advantage and completeness of models as the most unlikely advantage. This stands in contrast to an initial experiment revealing that modelers, in fact, increase the completeness of their models when adequate knowledge is presented while time consumption is not necessarily reduced. We explain possible causes of this mismatch and finally hypothesize on two “sweet spots” of process modeling recommender systems.
Ranganath Ashok Kumar
Full Text Available Smart devices in the hands of people are revolutionizing the social lifestyle of one's self. Everyone across the world are using smart devices linked to their social networking activities one such activity is to share location data by uploading the tagged media content like photos, videos. The data is of surroundings, events attended/attending and travel experiences. Users share their experiences at a given location through localization techniques. Using such data from social networks an attempt is made to analyse tagged media content to acquire information on user context, individual’s interests, tastes, behaviours and derive meaningful relationships amongst them are referred to as Location Based Social Networks (LBSNs. The resulting information can be used to market a product and to improve business, as well recommend a travel and plan an itinerary. This paper presents a comprehensive survey of recommended systems for LBSNs covering the concepts of LBSNs, terminologies of LBSN and various recommendation systems.
Steven Tom; Dale Christiansen; Dan Berrett
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.
Song Jin Bao
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.
Symeonidis, Panagiotis; Manolopoulos, Yannis
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...
Dokoohaki, Nima; Kaleli, Cihan; Polat, Huseyin; Matskin, Mihhail
Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommender's accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.
Manouselis, Nikos; Verbert, Katrien; Drachsler, Hendrik; Santos, Olga
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
Verbert, Katrien; Drachsler, Hendrik; Manouselis, Nikos; Wolpers, Martin; Vuorikari, Riina; Duval, Erik
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February,
Mohamadreza Karimi Alavije
Full Text Available Recommender systems provide personalised recommendations to users, helping them find their ideal items, also play a key role in encouraging users to make their purchases through websites thus leading to the success of online stores. The collaborative filtering method is one of the most successful techniques utilized in these systems facilitating the provision of recommendations close to that of the customer's taste and need. However the proliferation of both customers and products on offer, the technique faces some issues such as "cold start" and scalability. As such in this paper a new method has been introduced in which user-based collaborative filtering is used at a base method along with a weighted clustering of users based upon demographics in order to improve the results obtained from the system. The implementation of the results of the algorithms demonstrate that the presented approach has a lower RMSE, which means that the system offers improved performance and accuracy and that the resulting recommendations are closer to the taste and preferences of the users.
Jeckmans, Arjan; Peter, Andreas; Hartel, Pieter H.
Recommender systems can help users to find interesting content, often based on similarity with other users. However, studies have shown that in some cases familiarity gives comparable results to similarity. Using familiarity has the added bonus of increasing privacy between users and utilizing a
Yu, Junliang; Gao, Min; Rong, Wenge; Li, Wentao; Xiong, Qingyu; Wen, Junhao
With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.
In this thesis we report the results of our research on recommender systems, which addresses some of the critical scientific challenges that still remain open in this domain. Collaborative filtering (CF) is the most common technique of predicting the interests of a user by collecting preference
The 2D implicit hydrodynamical solver developed by Hujeirat & Rannacher is now modified to include the effects of radiation, magnetic fields and self-gravity in different geometries. The underlying numerical concept is based on the operator splitting approach, and the resulting 2D matrices are inverted using different efficient preconditionings such as ADI (alternating direction implicit), the approximate factorization method and Line-Gauss-Seidel or similar iteration procedures. Second-order finite volume with third-order upwinding and second-order time discretization is used. To speed up convergence and enhance efficiency we have incorporated an adaptive time-step control and monotonic multilevel grid distributions as well as vectorizing the code. Test calculations had shown that it requires only 38 per cent more computational effort than its explicit counterpart, whereas its range of application to astrophysical problems is much larger. For example, strongly time-dependent, quasi-stationary and steady-state solutions for the set of Euler and Navier-Stokes equations can now be sought on a non-linearly distributed and strongly stretched mesh. As most of the numerical techniques used to build up this algorithm have been described by Hujeirat & Rannacher in an earlier paper, we focus in this paper on the inclusion of self-gravity, radiation and magnetic fields. Strategies for satisfying the condition ∇.B=0 in the implicit evolution of MHD flows are given. A new discretization strategy for the vector potential which allows alternating use of the direct method is prescribed. We investigate the efficiencies of several 2D solvers for a Poisson-like equation and compare their convergence rates. We provide a splitting approach for the radiative flux within the FLD (flux-limited diffusion) approximation to enhance consistency and accuracy between regions of different optical depths. The results of some test problems are presented to demonstrate the accuracy and
Bin Dai; Rung-Ching Chen; Shun-Zhi Zhu; Chung-Yi Huang
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 a...
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.
Trave-Massuyes, L. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Milne, R.
We are interested in the monitoring and diagnosis of dynamic systems. In our work, we are combining explicit temporal models of the behaviour of a dynamic system with implicit behavioural models supporting model based approaches. This work is drive by the needs of and applied to, two gas turbines of very different size and power. In this paper we describe the problems of building systems for these domains and illustrate how we have developed a system where these two approaches complement each other to provide a comprehensive fault detection and diagnosis system. We also explore the strengths and weaknesses of each approach. The work described here is currently working continuously, on line to a gas turbine in a major chemical plant. (author) 24 refs.
Trave-Massuyes, L [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Milne, R
We are interested in the monitoring and diagnosis of dynamic systems. In our work, we are combining explicit temporal models of the behaviour of a dynamic system with implicit behavioural models supporting model based approaches. This work is drive by the needs of and applied to, two gas turbines of very different size and power. In this paper we describe the problems of building systems for these domains and illustrate how we have developed a system where these two approaches complement each other to provide a comprehensive fault detection and diagnosis system. We also explore the strengths and weaknesses of each approach. The work described here is currently working continuously, on line to a gas turbine in a major chemical plant. (author) 24 refs.
Takegawa, Kazuki; Hijikata, Yoshinori; Nishida, Shogo
Recently, the turn volume of music data on the Internet has increased rapidly. This has increased the user's cost to find music data suiting their preference from such a large data set. We propose a content-based music search and recommendation system. This system has an interface for searching and finding music data and an interface for editing a user profile which is necessary for music recommendation. By exploiting the visualization of the feature space of music and the visualization of the user profile, the user can search music data and edit the user profile. Furthermore, by exploiting the infomation which can be acquired from each visualized object in a mutually complementary manner, we make it easier for the user to search music data and edit the user profile. Concretely, the system gives to the user an information obtained from the user profile when searching music data and an information obtained from the feature space of music when editing the user profile.
Tanaka, Katsuaki; Hori, Koichi; Yamamoto, Masato
The flood of information on the Internet makes a person who surf it without some strong intention strayed into it. One of the ways to control the balance between a person and the flood is a recommender system by computer, and many web sites use it. These systems work on a web site for the same kind of items. However the field of personal activity is not limited to handle the same kind of thing and a web site, but also offline area in the real world. To handle personal offline activities, LifeLog is proposed as method to record it, but the main purpose of LifeLog is recording a personal history. How to use a history has still been studied stage. The authors have developed a recommender system that captures personal context from history of personal online and offline activities, treats information on web sites as a large set of context, and finds out and extends overlap of them, then recommends information located there. The aim of the system is that a person can enjoy waves of information again. The system worked as a part of My-life Assist Service. It was a service for mobile phones provided by NTT DoCoMo, Inc. as a field experiment from Dec. 2007 to Feb. 2008.
Sutherland, A.A.; Robertson, B.C.; Drobny, N.L.
One of the early important steps in the evolution of a low-level waste Compact is the development of a Regional Management Plan. Part of the Regional Management Plan is a description of the waste management system that indicates what kinds of facilities that will be available within the compact's region. The facilities in the waste management system can include those for storage, treatment and disposal of low-level radioactive waste. The Regional Management Plan also describes the number of facilities that will be operated simultaneously. This paper outlines the development of the recommended waste management system for the Midwest Compact. It describes the way a data base on low-level radioactive waste from the Compact was collected and placed into a computerized data base management system, and how that data base was subsequently used to analyze various options for treatment and disposal of low-level radioactive waste within the Midwest Compact. The paper indicates the thought process that led to the definition of four recommended waste management systems. Six methods for reducing the volume of waste to be disposed of in the Midwest Compact were considered. Major attention was focused on the use of regional compaction or incineration facilities. Seven disposal technologies, all different from the shallow land burial currently practiced, were also considered for the waste management system. After evaluating the options available, the Compact Commissioners recommended four waste disposal technologies--above-ground vaults, below-ground vaults, concrete canisters placed above ground, and concrete canisters placed below ground--to the host state that will be chosen in 1987. The Commissioners did not recommend use of a regional waste treatment facility
Full Text Available In this Internet age, recommender systems (RS have become popular, offering new opportunities and challenges to the business world. With a continuous increase in global competition, e-businesses, information portals, social networks and more, websites are required to become more user-centric and rely on the presence and role of RS in assisting users in better decision making. However, with continuous changes in user interests and consumer behavior patterns that are influenced by easy access to vast information and social factors, raising the quality of recommendations has become a challenge for recommender systems. There is a pressing need for exploring hybrid models of the five main types of RS, namely collaborative, demographic, utility, content and knowledge based approaches along with advancements in Big Data (BD to become more context-aware of the technology and social changes and to behave intelligently. There is a gap in literature with a research focus in this direction. This paper takes a step to address this by exploring a new paradigm of applying business intelligence (BI concepts to RS for intelligently responding to user changes and business complexities. A BI based framework adopting a hybrid methodology for RS is proposed with a focus on enhancing the RS performance. Such a business intelligent recommender system (BIRS can adopt On-line Analytical Processing (OLAP tools and performance monitoring metrics using data mining techniques of BI to enhance its own learning, user profiling and predictive models for making a more useful set of personalised recommendations to its users. The application of the proposed framework to a B2C e-commerce case example is presented.
Ito, Takahiro; Mita, Akira
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.
Zhao, Yao-Dong; Cai, Shi-Min; Tang, Ming; Shang, Min-Sheng
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.
Full Text Available In recent years, recommender systems (RS provide a considerable progress to users. RSs reduce the cost of a user’s time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active and their preferences are more difficult to detect. The aim of this study is to provide a new way to improve recall and precision in recommender systems for cold users. According to the available categories of items, prioritization of the proposed items is improved and then presented to the cold user. The obtained results show that in addition to increased speed of processing, recall and precision have an acceptable improvement.
Seko, Atsuto; Hayashi, Hiroyuki; Tanaka, Isao
Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where crystals can be formed [i.e., chemically relevant compositions (CRCs)]. In addition to data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge is helpful for the discovery of new compounds. We validate our recommender systems in two ways. First, one database is used to construct a model, while another is used for the validation. Second, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.
Stekh, Yu.; Artsibasov, V.
In this article adaptive clustering algorithm for recommender systems is developed. Розроблено адаптивний алгоритм кластеризації для рекомендаційних систем.
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....
Posner, E. C.; Stevens, R.
NASA is participating in the development of international standards for space data systems. Recommendations for standards thus far developed are assessed. The proposed standards for telemetry coding and packet telemetry provide worthwhile benefit to the DSN; their cost impact to the DSN should be small. Because of their advantage to the NASA space exploration program, their adoption should be supported by TDA, JPL, and OSTDS.
Chulmo Koo; Namho Chung; Juyeon Ham
Under the paradigm shift toward smart tourism, the exhibition industry is making efforts to introduce innovative technologies that can provide more diverse and valuable experiences to attendees. However, various new information technologies have failed in a market in practice due to the user’s resistance against it. Since innovative technology, such as booth recommender systems (BRS), is changing, creating uncertainty among consumers, consumer’s resistance to innovative technology can be cons...
Devaraju, Anusuriya; Jayasinghe, Gaya; Klump, Jens; Hogan, Dominic
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
Teeples, Ronald; Glyer, David
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.
Virvou, Maria; Jain, Lakhmi
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...
A. A. Salama
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.
Full Text Available Accuracy and diversity are two important aspects to evaluate the performance of recommender systems. Two diffusion-based methods were proposed respectively inspired by the mass diffusion (MD and heat conduction (HC processes on networks. It has been pointed out that MD has high recommendation accuracy yet low diversity, while HC succeeds in seeking out novel or niche items but with relatively low accuracy. The accuracy-diversity dilemma is a long-term challenge in recommender systems. To solve this problem, we introduced a background temperature by adding a ground user who connects to all the items in the user-item bipartite network. Performing the HC algorithm on the network with ground user (GHC, it showed that the accuracy can be largely improved while keeping the diversity. Furthermore, we proposed a weighted form of the ground user (WGHC by assigning some weights to the newly added links between the ground user and the items. By turning the weight as a free parameter, an optimal value subject to the highest accuracy is obtained. Experimental results on three benchmark data sets showed that the WGHC outperforms the state-of-the-art method MD for both accuracy and diversity.
Zhou, Yanbo; Lü, Linyuan; Liu, Weiping; Zhang, Jianlin
Accuracy and diversity are two important aspects to evaluate the performance of recommender systems. Two diffusion-based methods were proposed respectively inspired by the mass diffusion (MD) and heat conduction (HC) processes on networks. It has been pointed out that MD has high recommendation accuracy yet low diversity, while HC succeeds in seeking out novel or niche items but with relatively low accuracy. The accuracy-diversity dilemma is a long-term challenge in recommender systems. To solve this problem, we introduced a background temperature by adding a ground user who connects to all the items in the user-item bipartite network. Performing the HC algorithm on the network with ground user (GHC), it showed that the accuracy can be largely improved while keeping the diversity. Furthermore, we proposed a weighted form of the ground user (WGHC) by assigning some weights to the newly added links between the ground user and the items. By turning the weight as a free parameter, an optimal value subject to the highest accuracy is obtained. Experimental results on three benchmark data sets showed that the WGHC outperforms the state-of-the-art method MD for both accuracy and diversity.
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.
Rocha, L. M. (Luis Mateus)
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.
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.
Walsh, Ryan R; Krismer, Florian; Galpern, Wendy R; Wenning, Gregor K; Low, Phillip A; Halliday, Glenda; Koroshetz, Walter J; Holton, Janice; Quinn, Niall P; Rascol, Olivier; Shaw, Leslie M; Eidelberg, David; Bower, Pam; Cummings, Jeffrey L; Abler, Victor; Biedenharn, Judy; Bitan, Gal; Brooks, David J; Brundin, Patrik; Fernandez, Hubert; Fortier, Philip; Freeman, Roy; Gasser, Thomas; Hewitt, Art; Höglinger, Günter U; Huentelman, Matt J; Jensen, Poul H; Jeromin, Andreas; Kang, Un Jung; Kaufmann, Horacio; Kellerman, Lawrence; Khurana, Vikram; Klockgether, Thomas; Kim, Woojin Scott; Langer, Carol; LeWitt, Peter; Masliah, Eliezer; Meissner, Wassilios; Melki, Ronald; Ostrowitzki, Susanne; Piantadosi, Steven; Poewe, Werner; Robertson, David; Roemer, Cyndi; Schenk, Dale; Schlossmacher, Michael; Schmahmann, Jeremy D; Seppi, Klaus; Shih, Lily; Siderowf, Andrew; Stebbins, Glenn T; Stefanova, Nadia; Tsuji, Shoji; Sutton, Sharon; Zhang, Jing
Multiple system atrophy (MSA) is a rare neurodegenerative disorder with substantial knowledge gaps despite recent gains in basic and clinical research. In order to make further advances, concerted international collaboration is vital. In 2014, an international meeting involving leaders in the field and MSA advocacy groups was convened in Las Vegas, Nevada, to identify critical research areas where consensus and progress was needed to improve understanding, diagnosis, and treatment of the disease. Eight topic areas were defined: pathogenesis, preclinical modeling, target identification, endophenotyping, clinical measures, imaging biomarkers, nonimaging biomarkers, treatments/trial designs, and patient advocacy. For each topic area, an expert served as a working group chair and each working group developed priority-ranked research recommendations with associated timelines and pathways to reach the intended goals. In this report, each groups' recommendations are provided. Copyright © 2017 American Academy of Neurology.
Tang, Tiffany Ya; Winoto, Pinata
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.
Hoagland, W.; Leach, S. [W. Hoagland and Associates, Boulder, CO (United States)
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.
Koenke, Edmund J.; Williams, Larry; Calafa, Caesar
The National Aeronautics and Space Administration (NASA) Advanced Air Transportation Technologies (AATT) project in cooperation with the Department of Transportation (DOT) Volpe National Transportation Systems Center (VNTSC) contracted with the System Resources Corporation (SRC) for the evaluation of the existing environment and the identification of user and service provider needs in the Gulf of Mexico low-altitude Offshore Sector. The results of this contractor activity are reported in the Gulf of Mexico Helicopter Offshore System Technologies Engineering Needs Assessment. A recommended system design and transition strategy was then developed to satisfy the identified needs within the constraints of the environment. This work, also performed under contract to NASA, is the subject of this report.
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.
Lai, Calvin; Nosek, Brian; Hoffman, Kelly
Implicit prejudice are social preferences that exist outside of conscious awareness or conscious control. We summarize evidence for three mechanisms that influence the expression of implicit prejudice: associative change, contextual change, and change in control over implicit prejudice. We then review the evidence (or lack thereof) for five open issues in implicit prejudice reduction research: 1) what shows effectiveness in real-world application; 2) what doesn’t work for implicit prejudice r...
Steinhoff, Jeff J.
The scope of this calculation is to determine ventilation system resistances, pressure drops, airflows, and operating cost estimates for the Site Recommendation (SR) design as detailed in the ''Site Recommendation Subsurface Layout'' (BSC (Bechtel SAIC Company) 2001a). The statutory limit for emplacement of waste in Yucca Mountain is 70,000 metric tons of uranium (MTU) and is considered the base case for this report. The objective is to determine the overall repository system ventilation flow network for the monitoring phase during normal operations and to provide a basis for the system description document design descriptions. Any values derived from this calculation will not be used to support construction, fabrication, or procurement. The work scope is identified in the ''Technical Work Plan for Subsurface Design Section FY01 Work Activities'' (CRWMS M and O 2001, pp. 6 and 13). In accordance with the technical work plan this calculation was prepared in accordance with AP-3.12Q, ''Calculations'' and other procedures invoked by AP-3.12Q. It also incorporates the procedure AP-SI1.Q, ''Software Management''
Arnesen, Kjell E; Erikssen, Jan; Stavem, Knut
In a system with implicit queue management, to examine gender and socioeconomic status as determinants of waiting time for inpatient surgery, after adjusting for other potential predictors. A cohort of 452 subjects was examined in outpatient clinics of a general hospital and referred to inpatient surgery. They were followed until scheduled hospital admission (n=396) or until the requested procedure no longer was relevant (n=56). We compared waiting time between groups from referral date until hospital admission, using Kaplan-Meier estimates of waiting times and log rank test. A Cox proportional hazards model was used for assessing the risk ratio (RR) of hospital admission for scheduled surgery. Gender and socioeconomic status could not explain variations in waiting time. However, patients with suspected/verified neoplastic disease or a risk of serious deterioration without treatment had markedly shorter waiting times than the reference groups, with adjusted RR (95% confidence intervals (95%CI)) of time to receiving in-patient surgery of 2.3 (1.7-3.0) and 2.0 (1.3-3.0), respectively. Being on sick leave was associated with shorter waiting time, adjusted RR of 1.7 (1.2-2.5). Referrals from within the hospital or other hospitals had also shorter waiting times than referrals from primary health care physicians, adjusted RR=1.4 (1.1-1.8). There was no evidence of bias against women or people in lower socioeconomic classes in this implicit queue management system. However, patients' access to inpatient surgery was associated with malignancy, prognosis, sick leave status, physician experience, referral pattern and the major diagnosis category.
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.
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.
Full Text Available Purpose – Recommender systems collect information about users and businesses and how they are related. Such relation is given in terms of reviews and votes on reviews. User reviews gather opinions, rating scores and review influence. The latter component is crucial for determining which users are more relevant in a recommender system, that is, the users whose reviews are more popular than the average user’s reviews. Design/methodology/approach – A model of measure of user influence is proposed based on review and social attributes of the user. User influence is also used for determining how influenced has been a business being based on popular reviews. Findings – Results indicate there is a connection between social attributes and user influence. Such results are relevant for marketing, credibility estimation and Sybil detections, among others. Originality/value – The proposed model allows search parameterization based on the social attribute weights of users, reviews and businesses. Such weights defines the relevance on each attribute, which can be adjusted according to the search needs. Popularity results are then a function of weight preferences on user, reviews and businesses data join.
Otsuka, Eriko; Wallace, Scott A; Chiu, David
Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests on-demand. For hashtag ranking, we propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness which is one of the key challenges in analyzing microblog data. Our system is built on top of Hadoop, a leading platform for distributed computing, to provide scalable performance using Map-Reduce. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations are more stable and reliable than ranking tags based on tweet content similarity. Our results show that HF-IHU can achieve over 30 % hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet. Furthermore, our method out-performs kNN, k-popularity, and Naïve Bayes by 69, 54, and 17 %, respectively, on recall of the top 200 hashtags.
Dietze, Stefan; Drachsler, Hendrik; Daniela, Giordano
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.
Yeung, C H; Cimini, G; Jin, C-H
We introduce a simple model to study movie competition in recommender systems. Movies of heterogeneous quality compete against each other through viewers' reviews and generate interesting dynamics at the box office. By assuming mean-field interactions between the competing movies, we show that the runaway effect of popularity spreading is triggered by defeating the average review score, leading to box-office hits: Popularity rises and peaks before fade-out. The average review score thus characterizes the critical movie quality necessary for transition from box-office bombs to blockbusters. The major factors affecting the critical review score are examined. By iterating the mean-field dynamical equations, we obtain qualitative agreements with simulations and real systems in the dynamical box-office forms, revealing the significant role of competition in understanding box-office dynamics.
Full Text Available Nowadays, consumers have a lot of choices. Electronic retailers offer a great variety of products. Because of this, there is a need for Recommender Systems. These systems aim to solve the problem of matching consumers with the most appealing products for them. They do this by analyzing either the products information details (Content Based methods or users social behavior (Collaborative Filtering. This paper describes the Collaborative Filtering technique in more detail. It then presents one of the best methods for CF: the Matrix Factorization technique. Next, it presents two algorithms used for matrix factorization. Then, the paper describes the implementation details of a framework created by us, called Rho, that uses Collaborative Filtering. In the end, we present some results obtained after experimenting with this framework.
Yeung, C. H.; Cimini, G.; Jin, C.-H.
We introduce a simple model to study movie competition in recommender systems. Movies of heterogeneous quality compete against each other through viewers’ reviews and generate interesting dynamics at the box office. By assuming mean-field interactions between the competing movies, we show that the runaway effect of popularity spreading is triggered by defeating the average review score, leading to box-office hits: Popularity rises and peaks before fade-out. The average review score thus characterizes the critical movie quality necessary for transition from box-office bombs to blockbusters. The major factors affecting the critical review score are examined. By iterating the mean-field dynamical equations, we obtain qualitative agreements with simulations and real systems in the dynamical box-office forms, revealing the significant role of competition in understanding box-office dynamics.
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.
E. Camporeale (Enrico); G.L. Delzanno; B.K. Bergen; J.D. Moulton
htmlabstractWe describe a spectral method for the numerical solution of the Vlasov–Poisson system where the velocity space is decomposed by means of an Hermite basis, and the configuration space is discretized via a Fourier decomposition. The novelty of our approach is an implicit time
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.
O'Shea, Brian; Watson, Derrick G; Brown, Gordon D A
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 (c) 2016 APA, all rights reserved).
Valyrakis, Manousos; Basnet, Bipin; Dunsmore, Ian
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.
LaChance, J.; Whitehead, D.; Drouin, M.
In August 1995, the Nuclear Regulatory Commission (NRC) issued a policy statement proposing improved regulatory decisionmaking by increasing the use of PRA [probabilistic risk assessment] in all regulatory matters to the extent supported by the state-of-the-art in PRA methods and data. A key aspect in using PRA in risk-informed regulatory activities is establishing the appropriate scope and attributes of the PRA. In this regard, ASME decided to develop a consensus PRA Standard. The objective is to develop a PRA Standard such that the technical quality of nuclear plant PRAs will be sufficient to support risk-informed regulatory applications. This paper presents examples recommendations for the systems analysis element of a PRA for incorporation into the ASME PRA Standard
Houben, K.; Nosek, B.; Wiers, R.W.
Dual-process models propose that addictive behaviors are determined by an implicit, impulsive system and an explicit, reflective system. Consistent with these models, research has demonstrated implicit affective associations with alcohol, using the Implicit Association Test (IAT), that predict
Develay, Aude-Emmanuelle; Verdot, Charlotte; Grémy, Isabelle
This article presents the results of two studies designed to define the feasibility and framework of the future prison health monitoring system in France. The objective of the first study was to obtain the points of view of professionals involved in prison health and the second study was designed to assess the feasibility of using prisoner's medical files for epidemiological purposes. The point of view of various professionals was collected by questionnaire sent to 43 randomly selected prison physicians and by 22 semi-directive interviews. The feasibility study was based on analysis of the medical files of 330 randomly selected prisoners in eleven prisons chosen in order to reflect the diversity of correctional settings and prison populations. Additional interviews were conducted with the medical staff of these prison facilities. There is a consensus on the need to monitor prison health, but there are contrasting views on data collection methods (surveys or routinely collected data]. The feasibility study also showed that the implementation of a prison health monitoring system based on routinely collected data from prisoner's medical records was not feasible at the present time in France. In the light of these findings, it is recommended to initially develop a monitoring system based on regular nationwide surveys, while pursuing computerization and standardization of health data in prison.
Mafteiu-Scai Liviu Octavian
Full Text Available Partitioning the systems of equations is a very important process when solving it on a parallel computer. This paper presents some criteria which leads to more efficient parallelization, that must be taken into consideration. New criteria added to preconditioning process by reducing average bandwidth are pro- posed in this paper. These new criteria lead to a combination between preconditioning and partitioning of systems equations, so no need two distinct algorithms/processes. In our proposed methods - where the preconditioning is done by reducing the average bandwidth- two directions were followed in terms of partitioning: for a given preconditioned system determining the best partitioning (or one as close and the second consist in achieving an adequate preconditioning, depending on a given/desired partitioning. A mixed method it is also proposed. Experimental results, conclusions and recommendations, obtained after parallel implementation of conjugate gradient on IBM BlueGene /P supercomputer- based on a synchronous model of parallelization- are also presented in this paper.
Huizinga, H.P.; Schaling, E.; van der Windt, P.C.
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
The aims of this paper are to precise some essential conditions for building reuse models for hospital information systems (HIS) and to present an application for hospital clinical laboratories. Reusability is a general trend in software, however reuse can involve a more or less part of design, classes, programs; consequently, a project involving reusability must be precisely defined. In the introduction it is seen trends in software, the stakes of reuse models for HIS and the special use case constituted with a HIS. The main three parts of this paper are: 1) Designing a reuse model (which objects are common to several information systems?) 2) A reuse model for hospital clinical laboratories (a genspec object model is presented for all laboratories: biochemistry, bacteriology, parasitology, pharmacology, ...) 3) Recommendations for generating plug-compatible software components (a reuse model can be implemented as a framework, concrete factors that increase reusability are presented). In conclusion reusability is a subtle exercise of which project must be previously and carefully defined.
Pedro M. P. Rosa
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.
S. Wendel (Sonja); B.G.C. Dellaert (Benedict); A. Ronteltap (Amber); H.C.M. van Trijp (Hans)
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
Wendel, S.; Dellaert, B.G.C.; Ronteltap, A.; Trijp, van J.C.M.
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
S. Wendel (Sonja); B.G.C. Dellaert (Benedict); A. Ronteltap (Amber); H.C.M. van Trijp (Hans)
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
Roč. 69, č. 2 (2004), s. 387-397 ISSN 0022-4812 R&D Projects: GA AV ČR IAA1019901; GA MŠk LN00A056 Institutional research plan: CEZ:AV0Z1019905 Keywords : propositional proof system s * circuits * polynomial simulation Subject RIV: BA - General Mathematics Impact factor: 0.331, year: 2004
Eric Cornelius; Mark Fabro
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.
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
Jesus G. Boticario; Olga C. Santos
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 ...
Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.
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.
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.
Yang, J.; Sun, Zhu; Bozzon, A.; Zhang, J.; Larson, M.A.
The "International Workshop on Recommender Systems for Citizens" (CitRec) is focused on a novel type of recommender systems both in terms of ownership and purpose: recommender systems run by citizens and serving society as a whole.
Romero, C.; Ventura, S.; Delgado, J.A.; De Bra, P.M.E.; Duval, E.; Klamma, R.; Wolpers, M.
In this paper, we describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable educational hypermedia system. We present a specific mining tool and a recommender engine that we have integrated in the AHA! system
Site Recommendation (SR). The Total System Performance Assessment-Site Recommendation (TSPA-SR) will present a compliance evaluation of overall system performance against the guidelines and requirements in the revision of the DOE siting guidelines to be promulgated at 10 CFR 963, U.S. Nuclear Regulatory Commission (NRC) regulation for HLW disposal at proposed 10 CFR 63 (the proposed rule has been published at 64 FR 8640), and U.S. Environmental Protection Agency (EPA) environmental radiation protection standard to be promulgated at 40 CFR 197. At present, the NRC has issued the proposed 10 CFR 63 (64 FR 8640) for public comment whereas the EPA standard and the revised DOE siting guidelines are currently being developed. EPA has announced the release of 40 CFR 197 proposed rule on its website (www.epa.gov/radiation/yucca/rule.qui.htm) and the Federal Register announcement (which initiates the 90 day public comment period) is expected by the end of August, 1999. The purpose of this document is to present the overall goals, objectives, scope, methods, approach, and assumptions to be used in the development of the TSPA-SR. This document will serve as a communication tool for coordinating the DOE TSPA activities and for keeping the NRC staff informed of the TSPA activities for the SR.
Duan, L.; Street, W. N.; Xu, E.
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.
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.
Ravi, Logesh; Vairavasundaram, Subramaniyaswamy
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. PMID:27069468
Ravi, Logesh; Vairavasundaram, Subramaniyaswamy
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.
Knapp, Charles E. [Univ. of New Mexico, Albuquerque, NM (United States)
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.
This thesis using the method of research design is about creating a journal recommendation system for authors. Existing systems like JANE or whichjournal.com offer recommendations based on similarities of the content. This study invests how more sophisticated factors like openness, price (subscription or article processing charge), speed of publication can be included in the ranking of a recommendation system. The recommendation should also consider the expectations from other stakeholders li...
The unstoppable acceleration of the scientific and technological development that is revolutionizing our socioeconomic systems in recent years has made the critical aspects and the inadequacy of medical epistemology more and more evident. Several elements have underlined the insufficiency of traditional ethical points of reference in Medicine, like the change of individual needs, the technical possibility of long-term management of heavy diseases, the change of the social and health systems caused by the interaction of different ethnic groups and cultures, several problems linked to the fair distribution of resources in regime of fiscal scarcity involving all the industrialized countries of our world. This brought to the necessity for Medicine to modify its coordinates, adjusting them on the person, and not on the disease. In order to reach this objective, the author strategically suggests Systemics as the epistemological guidance of the knowledge process, which can make the scientific method operate in an ethical and cultural horizon centered on the human being valorization, on the respect of his/her needs and the respect of his/her environment. A systemic approach of the medical thought can allow the re-orientation of the clinical look from a biological to a biographic one, the re-definition of the aim of the medical intervention as the restoration and support of self-organizing and self-regulating processes of the biological system, the achieving of a social and health expenditure's saving through a major appropriateness of prescription and an inherent preventive valence of medical interventions, the offer of new and larger horizons for the development of scientific research.
Journal of Fundamental and Applied Sciences ... Then, we analyze and discuss based on a set of defined characteristics the use of recommendation ... which seems the most suitable to meet our qualitative approach in this specific context.
Heidelberger, N.; Karpinnen, K.; D'Acunto, L.
Personalized recommendations in search engines, social media and also in more traditional media increasingly raise concerns over potentially negative consequences for diversity and the quality of public discourse. The algorithmic filtering and adaption of online content to personal preferences and
Oliver J. D. Barrowclough
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.
Full Text Available Under the paradigm shift toward smart tourism, the exhibition industry is making efforts to introduce innovative technologies that can provide more diverse and valuable experiences to attendees. However, various new information technologies have failed in a market in practice due to the user’s resistance against it. Since innovative technology, such as booth recommender systems (BRS, is changing, creating uncertainty among consumers, consumer’s resistance to innovative technology can be considered a normal reaction. Therefore, it is important for a company to understand the psychological aspect of the consumer’s resistance and make measures to overcome the resistance. Accordingly, based on the model of Kim and Kankanhalli (2009, by applying the perceived value, the technology acceptance model, and the status quo bias theory, this study focused on the importance of self-efficacy and technical support in the context of using BRS. To do this purpose, a total of 455 survey data that was collected from “Korea franchise exhibition” attendees were used to analyze the proposed model. Structural equation modeling was applied for data analysis. The result shows that perceived value was affected by relative advantage and switching cost, also switching cost reduced the perceived value. However, self-efficacy reduced the switching cost, thereby decreasing the resistance of exhibition attendees. In addition, technical support increased the relative advantage switching cost and the perceived value. Exhibition attendee’s resistance was significantly negatively affected by perceived value, and positively affected by switching cost. The results will provide balanced viewpoints between the relative advantage and switching cost for exhibition marketers, helping to strengthen the competitiveness in terms of sustainable tourism of exhibition.
Jesus G. Boticario
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.
Ghauth, Khairil Imran; Abdullah, Nor Aniza
One of the anticipated challenges of today's e-learning is to solve the problem of recommending from a large number of learning materials. In this study, we introduce a novel architecture for an e-learning recommender system. More specifically, this paper comprises the following phases i) to propose an e-learning recommender system based on…
Ward, Emma V; Berry, Christopher J; Shanks, David R
Recognition memory is typically weaker in healthy older relative to young adults, while performance on implicit tests (e.g., repetition priming) is often comparable between groups. Such observations are commonly taken as evidence for independent explicit and implicit memory systems. On a picture version of the continuous identification with recognition (CID-R) task, we found a reliable age-related reduction in recognition memory, while the age effect on priming did not reach statistical significance (Experiment 1). This pattern was consistent with the predictions of a formal single-system model. Experiment 2 replicated these observations using separate priming (continuous identification; CID) and recognition phases, while a combined data analysis revealed a significant effect of age on priming. In Experiment 3, we provide evidence that priming in this task is unaffected by explicit processing, and we conclude that the age difference in priming is unlikely to have been driven by differences in explicit processing between groups of young and older adults ("explicit contamination"). The results support the view that explicit and implicit expressions of memory are driven by a single underlying memory system. PsycINFO Database Record (c) 2013 APA, all rights reserved.
... SOCIAL SECURITY ADMINISTRATION [Docket No. SSA-2010-0066] Proposed Recommendation to the Social Security Administration for Occupational Information System (OIS) Development Planning; Request for Comment...) to provide independent advice and recommendations on plans and activities to create an occupational...
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.
John Tarus; Zhendong Niu; Bakhti Khadidja
In recent years, e-learning recommender systems has attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. E-learning recommenders continue to play an increasing educational role in aiding learners to find appropriate learning materials to support the achievement of their learning goals. Although general recommender systems have recorded significant success in solving ...
Oduwobi, Olukunle; Ojokoh, Bolanle Adefowoke
Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new…
Knijnenburg, B.P.; Willemsen, M.C.
In a recommender system that suggests options based on user attribute weights, the method of preference elicitation (PE) employed by a recommender system can influence users' satisfaction with the system, as well as the perceived usefulness and the understandability of the system. Specifically, we
Brusilovsky, Peter; De Gemmis, Marco; Felfernig, Alexander; Lops, Pasquale; O'Donovan, John; Tintarev, Nava; Willemsen, Martijn
As intelligent interactive systems, recommender systems focus on determining predictions thatfit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way
Brusilovsky, P.; Felfernig, A.; Lops, P.; O'Donovan, J.; Semeraro, G.; Tintarev, N.; Willemsen, M.C.
As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which
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.
Alphy, Anna; Prabakaran, S
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.
Roskos-Ewoldsen, David R.
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…
Ben Othmane , Amel; Tettamanzi , Andrea G. B.; Villata , Serena; Le Thanh , Nhan
International audience; In this paper, a simulation of a multi-agent recommender system is presented and developed in the NetLogo platform. The specification of this recommender system is based on the well known Belief-Desire-Intention agent architecture applied to multi-context systems, extended with contexts foradditional reasoning abilities, especially social ones. The main goal of this simulation study is, besides illustrating the usefulness and feasibility of our agent-based recommender ...
Anderson, Joel; Antalikova, Radka
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...
Chu, Chunlei; Stoffa, Paul L.
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.
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.
Waleed M. Al-Adrousy
Full Text Available Mobile social networking is a new trend for social networking that enables users with similar interests to connect together through mobile devices. Therefore, it possesses the same features of a social network with added support to the features of a Mobile Ad-hoc Network (MANET in terms of limited computing power, limited coverage, and intermittent connectivity. One of the most important features in social networks is Team Formation. Team Formation aims to assemble a set of users with a set of skills required for a certain task. The team formation is a special type of recommendation which is important to enable cooperative work among users. Team formation is challenging since users’ interaction time is limited in MANET. The main objective of this paper is to introduce a peer-to-peer team formation technique based on zone routing protocol (ZRP. A comparison was made with Flooding and Adaptive Location Aided Mobile Ad Hoc Network Routing (ALARM techniques. The suggested technique achieves fast successful recommendations within the limited mobile resources and reduces exchanged messages. The suggested technique has fast response time, small required buffering and low power consumption. The testing results show better performance of the suggested technique compared to flooding and ALARM technique.
Dodge, Catherine A.
Approved for public release; distribution in unlimited. While a necessity of all operating systems, the code that initializes a system can be notoriously difficult to understand. This thesis explores the most common architectures used for bringing an operating system to its initial state, once the operating system gains control from the boot loader. Specifically, the ways in which the OpenBSD and Linux operating systems handle initialization are dissected. With this understanding, a set ...
Seko, Atsuto; Hayashi, Hiroyuki; Kashima, Hisashi; Tanaka, Isao
Chemically relevant compositions (CRCs) and atomic arrangements of inorganic compounds have been collected as inorganic crystal structure databases. Machine learning is a unique approach to search for currently unknown CRCs from vast candidates. Herein we propose matrix- and tensor-based recommender system approaches to predict currently unknown CRCs from database entries of CRCs. Firstly, the performance of the recommender system approaches to discover currently unknown CRCs is examined. A Tucker decomposition recommender system shows the best discovery rate of CRCs as the majority of the top 100 recommended ternary and quaternary compositions correspond to CRCs. Secondly, systematic density functional theory (DFT) calculations are performed to investigate the phase stability of the recommended compositions. The phase stability of the 27 compositions reveals that 23 currently unknown compounds are newly found to be stable. These results indicate that the recommender system has great potential to accelerate the discovery of new compounds.
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
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%.
Hahn, Adam; Judd, Charles M.; Hirsh, Holen K.; Blair, Irene V.
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
T N Chiranjeevi; R H Vishwanath
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 respe...
Durao, Frederico; Dolog, Peter
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...... system which suggests similar Web pages based on the similarity of their tags. Tagging behavior and language anomalies in tagging activities are some aspects examined from an experiment involving 38 people from 12 countries....
Real-time reactor simulator had been developed by reusing the equipment of the Musashi reactor and its performance improvement became indispensable for research tools to increase sampling rate with introduction of arithmetic units using multi-Digital Signal Processor(DSP) system (cluster). In order to realize the heterogeneous cluster type multi-processor system computing, combination of two kinds of Control Processor (CP) s, Cluster Control Processor (CCP) and System Control Processor (SCP), were proposed with Large System Control Processor (LSCP) for hierarchical cluster if needed. Faster computing performance of this system was well evaluated by simulation results for simultaneous execution of plural jobs and also pipeline processing between clusters, which showed the system led to effective use of existing system and enhancement of the cost performance. (T. Tanaka)
Bahramian, Z.; Abbaspour, R. Ali
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.
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.
Guthrey, Pierson Tyler
The relativistic Vlasov-Maxwell system (RVM) models the behavior of collisionless plasma, where electrons and ions interact via the electromagnetic fields they generate. In the RVM system, electrons could accelerate to significant fractions of the speed of light. An idea that is actively being pursued by several research groups around the globe is to accelerate electrons to relativistic speeds by hitting a plasma with an intense laser beam. As the laser beam passes through the plasma it creates plasma wakes, much like a ship passing through water, which can trap electrons and push them to relativistic speeds. Such setups are known as laser wakefield accelerators, and have the potential to yield particle accelerators that are significantly smaller than those currently in use. Ultimately, the goal of such research is to harness the resulting electron beams to generate electromagnetic waves that can be used in medical imaging applications. High-order accurate numerical discretizations of kinetic Vlasov plasma models are very effective at yielding low-noise plasma simulations, but are computationally expensive to solve because of the high dimensionality. In addition to the general difficulties inherent to numerically simulating Vlasov models, the relativistic Vlasov-Maxwell system has unique challenges not present in the non-relativistic case. One such issue is that operator splitting of the phase gradient leads to potential instabilities, thus we require an alternative to operator splitting of the phase. The goal of the current work is to develop a new class of high-order accurate numerical methods for solving kinetic Vlasov models of plasma. The main discretization in configuration space is handled via a high-order finite element method called the discontinuous Galerkin method (DG). One difficulty is that standard explicit time-stepping methods for DG suffer from time-step restrictions that are significantly worse than what a simple Courant-Friedrichs-Lewy (CFL
Ettlinger, Marc; Margulis, Elizabeth H; Wong, Patrick C M
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.
Sandy, Heleau; Drachsler, Hendrik; Gillet, Dennis
Heleou, S., Drachsler, H., & Gillet, D. (2009). Evaluation of Recommender Systems for Technology-Enhanced Learning: Challenges and Possible Solutions. 1st workshop on Context-aware Recommender Systems for Learning at the Alpine Rendez-Vous. November, 30-December, 3, 2009, Garmisch-Patenkirchen,
Drachsler, Hendrik; Manouselis, Nikos
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,
Knijnenburg, B.P.; Willemsen, M.C.
To increase the user experience, preference elicitation methods used by recommender systems can be adapted to individual differences such as the level of expertise. However, we will show that the satisfaction and perceived usefulness of a recommender system also depends strongly on subtle variations
Knijnenburg, B.P.; Reijmer, N.J.M.; Willemsen, M.C.; Mobasher, B.; Burke, R.
This paper compares five different ways of interacting with an attribute-based recommender system and shows that different types of users prefer different interaction methods. In an online experiment with an energy-saving recommender system the interaction methods are compared in terms of perceived
Shereen H. Ali
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.
Benhamdi, Soulef; Babouri, Abdesselam; Chiky, Raja
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…
Kraus, Alexandra; Scholderer, Joachim
According to recent neurobiological models, food choices are influenced by two separate reward systems: motivational wanting (incentive salience of the reward) and affective liking (hedonic pleasure associated with the reward). Both are assumed to have conscious and unconscious components. Applying...... 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...
Binoy B. Nair
Full Text Available Recommender systems capable of discovering patterns in stock price movements and generating stock recommendations based on the patterns thus discovered can significantly supplement the decision-making process of a stock trader. Such recommender systems are of great significance to a layperson who wishes to profit by stock trading even while not possessing the skill or expertise of a seasoned trader. A genetic algorithm optimized Symbolic Aggregate approXimation (SAX–Apriori based stock trading recommender system, which can mine temporal association rules from the stock price data set to generate stock trading recommendations, is presented in this article. The proposed system is validated on 12 different data sets. The results indicate that the proposed system significantly outperforms the passive buy-and-hold strategy, offering scope for a layperson to successfully invest in capital markets.
T N Chiranjeevi
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
Van Luik, Abraham E.
The session started with Abe Van Luik (IGSC Chair, US-DOE-YM, USA) who presented the feedback of the international peer review of the US-DOE Yucca Mountain TSPA (Total System Performance Assessment) supporting the successful designation of the site by the Congress and the President of the U.S. In particular, he listed key implications of the IRT (International Review team) recommendations on the forthcoming US-DOE documentation of its case for safety to be submitted to the regulator, the U.S. Nuclear Regulatory Commission, mainly: - The documentation submitted to the licensing authority should address technical aspects and compliance with regulatory criteria. - That documentation should reflect sound science and good engineering practice; it should present detailed and rigorous modelling. - In addition, it should present both quantitative and qualitative arguments, make a statement on why there can be confidence in the face of uncertainty, acknowledge remaining issues and provide the strategy to resolve them. - Demonstrating understanding is as important as demonstrating compliance. - There is a need to provide a clear explanation of the case made to the regulator for more general audiences to complement the large amount of technical documents that will be produced. The US-DOE response to these recommendations for the License Application, which is under preparation, is that the recommendations will be implemented to the maximum extent possible. In subsequent discussion, with respect to the License Application, it was acknowledged that detailed guidance from the U.S. regulator was very useful, and guidance of this type would be generally useful. At the current time, the words 'safety case' are not mentioned in U.S. regulations, but if one reads both the regulation and guidance documents it becomes evident that all aspects of a safety case need to be provided in the License Application and its accompanying documents
Wendel, S.; Ronteltap, A.; Dellaert, B.G.C.; Trijp, van J.C.M.
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
...) contracted with the System Resources Corporation (SRC) for the evaluation of the existing environment and the identification of user and service provider needs in the Gulf of Mexico low-altitude Offshore Sector...
Florida, St. Petersburg, FL, 123 pp. Koch, E. W. 1994. Hydrodynamics, diffusion boundary layers and photosynthesis of the seagrasses Thalassia testudinum...ER D C TR -0 6- 15 System-Wide Water Resources Program Submerged Aquatic Vegetation Restoration Research Program Waves in Seagrass ...Water Resources Research Program and Submerged Aquatic Vegetation Restoration Research Program ERDC TR-06-15 November 2006 Waves in Seagrass Systems
Soerenssen, Aimar; Veland, Oeystein; Farbrot, Jan Erik; Kaarstad, Magnhild; Seim, Lars Aage; Foerdestroemmen, Nils; Bye, Andreas
Alarm systems have been of major concern within complex industrial processes for many years. Within the nuclear community, the TMI accident in 1979 was the first really serious event that showed also the importance of the man-machine aspects of the systems in general, and the alarm system in particular. The OECD Halden Reactor Project has been working with alarm systems since 1974. This report is an attempt to gather some of the knowledge that has been accumulated during the years in Halden, both in research and also in bilateral projects. Bilateral projects within this field have provided a practical basis of knowledge.A major part of this report consists of a set of recommendations, which reflect HRP's current understanding of how an alarm system should work. There are also recommendations on design methods. But also other issues are included, as system development and implementation experience, and experimental knowledge on the performance of alarm systems. Some open issues are also discussed. (Author). 54 refs., 15 figs
Interesting modelling of intense electron flow has been done with implicit particle-in-cell simulation codes. In this report, the direct implicit PIC simulation approach is applied to simulations that include full electromagnetic fields. The resulting algorithm offers advantages relative to moment implicit electromagnetic algorithms and may help in our quest for robust and simpler implicit codes
Full Text Available Recommender systems are widely used, in social networks and online stores, to overcome the problems caused by the large amount of information. Most of these systems use a collaborative filtering method to generate recommendations to the users. But, as in this method users’ feedback is considered for recommendations, it can be significantly erroneous by the malicious people. In other words, there may be some users who open fake profiles and vote one-sided or biased in the system that may cause disturbance in providing proper recommendations to other users. This kind of damage is said to be shiling attacks. If the attackers succeed, the user's trust in the recommender systems will reduce. In recent years, efficient attack detection algorithms have been proposed, but each has its own limitations. In this paper, we use profile-based and item-based algorithms to provide a new mechanism to significantly reduce the detection error for shilling attacks.
The purpose is to determine the state-of-the-practice in Verification and Validation (V and V) of Expert Systems (ESs) on current NASA and Industry applications. This is the first task of a series which has the ultimate purpose of ensuring that adequate ES V and V tools and techniques are available for Space Station Knowledge Based Systems development. The strategy for determining the state-of-the-practice is to check how well each of the known ES V and V issues are being addressed and to what extent they have impacted the development of ESs.
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)
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.
van Setten, M.J.; Veenstra, M.; van Dijk, Elisabeth M.A.G.; Nijholt, Antinus; Isaísas, P.; Karmakar, N.
Predicting the interests of a user in information is an important process in personalized information systems. In this paper, we present a way to create prediction engines that allow prediction techniques to be easily combined into prediction strategies. Prediction strategies choose one or a
Hurtado, L.D.; Knowles, M.K.; Kelley, V.A.; Jones, T.L.; Ogintz, J.B.; Pfeifle, T.W.
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
Zheng, Xiang-wei; Ma, Hong-wei; Li, Yan
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…
Koschmider, A.; Song, M.S.; Reijers, H.A.; Abramowicz, W.
Social software is known to stimulate the exchange and sharing of information among peers. This paper describes how an existing system that supports process builders in completing a business process can be enhanced with various social features. In that way, it is easier for process modeler to become
Kucherbaev, Pavel; Psyllidis, Achilleas; Bozzon, Alessandro
In this paper, we outline the vision of chatbots that facilitate the interaction between citizens and policy-makers at the city scale. We report the results of a co-design session attended by more than 60 participants. We give an outlook of how some challenges associated with such chatbot systems could be addressed in the future.
Mix, Scott R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Kirkham, Harold [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Silverstein, Alison [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Compliance with the NERC requirements for Critical Infrastructure Protection (CIP) for synchrophasor systems in the Version 5 paradigm seems to be a matter of some uncertainty for those in the synchrophasor user community. This report aims to provide clarification and guidance in the form of case studies based on methods seen in the industry
Ronaldo Lima Rocha Campos
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.
Mukund, Nikhil; Thakur, Saurabh; Abraham, Sheelu; Aniyan, A. K.; Mitra, Sanjit; Sajeeth Philip, Ninan; Vaghmare, Kaustubh; Acharjya, D. P.
We present a machine-learning-based information retrieval system for astronomical observatories that tries to address user-defined queries related to an instrument. In the modern instrumentation scenario where heterogeneous systems and talents are simultaneously at work, the ability to supply people with the right information helps speed up the tasks for detector operation, maintenance, and upgradation. The proposed method analyzes existing documented efforts at the site to intelligently group related information to a query and to present it online to the user. The user in response can probe the suggested content and explore previously developed solutions or probable ways to address the present situation optimally. We demonstrate natural language-processing-backed knowledge rediscovery by making use of the open source logbook data from the Laser Interferometric Gravitational Observatory (LIGO). We implement and test a web application that incorporates the above idea for LIGO Livingston, LIGO Hanford, and Virgo observatories.
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.
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.
Dombrowski, H.; Bleher, M.; De Cort, M.; Dabrowski, R.; Neumaier, S.; Stöhlker, U.
After the Chernobyl nuclear power plant accident in 1986, followed by the Fukushima Nuclear power plant accident 25 years later, it became obvious that real-time information is required to quickly gain radiological information. As a consequence, the European countries established early warning network systems with the aim to provide an immediate warning in case of a major radiological emergency, to supply reliable information on area dose rates, contamination levels, radioactivity concentrations in air and finally to assess public exposure. This is relevant for governmental decisions on intervention measures in an emergency situation. Since different methods are used by national environmental monitoring systems to measure area dose rate values and activity concentrations, there are significant differences in the results provided by different countries. Because European and neighboring countries report area dose rate data to a central data base operated on behalf of the European Commission, the comparability of the data is crucial for its meaningful interpretation, especially in the case of a nuclear accident with transboundary implications. Only by harmonizing measuring methods and data evaluation, is the comparability of the dose rate data ensured. This publication concentrates on technical requirements and methods with the goal to effectively harmonize area dose rate monitoring data provided by automatic early warning network systems. The requirements and procedures laid down in this publication are based on studies within the MetroERM project, taking into account realistic technical approaches and tested procedures.
Marlin, Benjamin M; Adams, Roy J; Sadasivam, Rajani; Houston, Thomas K
The goal of computer tailored health communications (CTHC) is to promote healthy behaviors by sending messages tailored to individual patients. Current CTHC systems collect baseline patient "profiles" and then use expert-written, rule-based systems to target messages to subsets of patients. Our main interest in this work is the study of collaborative filtering-based CTHC systems that can learn to tailor future message selections to individual patients based explicit feedback about past message selections. This paper reports the results of a study designed to collect explicit feedback (ratings) regarding four aspects of messages from 100 subjects in the smoking cessation support domain. Our results show that most users have positive opinions of most messages and that the ratings for all four aspects of the messages are highly correlated with each other. Finally, we conduct a range of rating prediction experiments comparing several different model variations. Our results show that predicting future ratings based on each user's past ratings contributes the most to predictive accuracy.
Cunningham, William A; Nezlek, John B; Banaji, Mahzarin R
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.
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…
Kowalczyk, W.J.; Szlavik, Z.; Schut, M.C.
Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the
Geetha, G.; Safa, M.; Fancy, C.; Saranya, D.
In today’s digital world, it has become an irksome task to find the content of one's liking in an endless variety of content that are being consumed like books, videos, articles, movies, etc. On the other hand there has been an emerging growth among the digital content providers who want to engage as many users on their service as possible for the maximum time. This gave birth to the recommender system comes wherein the content providers recommend users the content according to the users’ taste and liking. In this paper we have proposed a movie recommendation system. A movie recommendation is important in our social life due to its features such as suggesting a set of movies to users based on their interest, or the popularities of the movies. In this paper we are proposing a movie recommendation system that has the ability to recommend movies to a new user as well as the other existing users. It mines movie databases to collect all the important information, such as, popularity and attractiveness, which are required for recommendation. We use content-based and collaborative filtering and also hybrid filtering, which is a combination of the results of these two techniques, to construct a system that provides more precise recommendations concerning movies.
Drachsler, Hendrik; Bogers, Toine; Vuorikari, Riina
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...
Zarrinkalam, Fattane; Kahani, Mohsen
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…
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.
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.
Klein, Gunnar O.; Smith, Barry
This essay concerns the problems surrounding the use of the term ``concept'' in current ontology and terminology research. It is based on the constructive dialogue between realist ontology on the one hand and the world of formal standardization of health informatics on the other, but its conclusions are not restricted to the domain of medicine. The term ``concept'' is one of the most misused even in literature and technical standards which attempt to bring clarity. In this paper we propose to use the term ``concept'' in the context of producing defined professional terminologies with one specific and consistent meaning which we propose for adoption as the agreed meaning of the term in future terminological research, and specifically in the development of formal terminologies to be used in computer systems. We also discuss and propose new definitions of a set of cognate terms. We describe the relations governing the realm of concepts, and compare these to the richer and more complex set of relations obtaining between entities in the real world. On this basis we also summarize an associated terminology for ontologies as representations of the real world and a partial mapping between the world of concepts and the world of reality.
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 consumers' intention to use such a health recommendation system as a function of options related to the underlying system (e.g. the type of company that generates the advice) as well as intermediaries (e.g. general practitioner) that might assist in using the system. We further explore if the effect of both the system and intermediaries on intention to use a health recommendation system are mediated by consumers' perceived effort, privacy risk, usefulness and enjoyment. Methods 204 respondents from a consumer panel in the Netherlands participated. The data were collected by means of a questionnaire. Each respondent evaluated three hypothetical health recommendation systems on validated multi-scale measures of effort, privacy risk, usefulness, enjoyment and intention to use the system. To test the hypothesized relationships we used regression analyses. Results We find evidence that the options related to the underlying system as well as the intermediaries involved influence consumers' intention to use such a health recommendation system and that these effects are mediated by perceptions of effort, privacy risk, usefulness and enjoyment. Also, we find that consumers value usefulness of a system more and enjoyment less when a general practitioner advices them to use a health recommendation system than if they use it out of their own curiosity. Conclusions We developed and tested a model of consumers' intention to use a health recommendation system. We found that intermediaries play an important role in how consumers evaluate such a system over and above options of the underlying system that is used to generate the recommendation. Also, health-related information services seem to
Wendel, Sonja; Dellaert, Benedict G C; Ronteltap, Amber; van Trijp, Hans C M
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 consumers' intention to use such a health recommendation system as a function of options related to the underlying system (e.g. the type of company that generates the advice) as well as intermediaries (e.g. general practitioner) that might assist in using the system. We further explore if the effect of both the system and intermediaries on intention to use a health recommendation system are mediated by consumers' perceived effort, privacy risk, usefulness and enjoyment. 204 respondents from a consumer panel in the Netherlands participated. The data were collected by means of a questionnaire. Each respondent evaluated three hypothetical health recommendation systems on validated multi-scale measures of effort, privacy risk, usefulness, enjoyment and intention to use the system. To test the hypothesized relationships we used regression analyses. We find evidence that the options related to the underlying system as well as the intermediaries involved influence consumers' intention to use such a health recommendation system and that these effects are mediated by perceptions of effort, privacy risk, usefulness and enjoyment. Also, we find that consumers value usefulness of a system more and enjoyment less when a general practitioner advices them to use a health recommendation system than if they use it out of their own curiosity. We developed and tested a model of consumers' intention to use a health recommendation system. We found that intermediaries play an important role in how consumers evaluate such a system over and above options of the underlying system that is used to generate the recommendation. Also, health-related information services seem to rely on endorsement by the medical sector
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.
Drachsler, Hendrik; Hummel, Hans; Koper, Rob
Drachsler, H., Hummel, H. G. K., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology, 3(4), 404-423.
Verbert, Katrien; Manouselis, Nikos; Xavier, Ochoa; Wolpers, Martin; Drachsler, Hendrik; Ivana, Bosnic; Erik, Duval
Verbert, K., Manouselis, N., Xavier, O., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (accepted). Context-aware Recommender Systems for Learning: a Survey and Future Challenges. IEEE Transactions on Learning Technologies (TLT).
Drachsler, Hendrik; Hummel, Hans; Koper, Rob
Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.
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
Liu, Xiao; Liu, An; Zhang, Xiangliang; Li, Zhixu; Liu, Guanfeng; Zhao, Lei; Zhou, Xiaofang
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
Full Text Available Studies were carried out to determine the effects of four fertilizer recommendation systems (bianket recommendation, soil test recommendation, recommendation based on nutrient supplementation index and unfertilized control on five cropping systems (sole cassava, maize, melon, cassava + maize and cassava + maize + melon. The experiment was a split-plot in randomised complete block design, with fertilizer recommendation systems in main plots and cropping systems in subplots. Observations were made on plant growth and yield. Plant samples were also analyzed for N, P and K uptake. Cassava and melon gave higher yields in sole cropping than intercropping while maize yield under intercropping exceeded that under sole cropping by 17 %. Cassava root yield was significantly reduced by 24 and 35 % in cassava + maize and cassava + maize + melon plots. Fertilizer recommendation based on nutrient supplementation index (NSI gave the highest crop yield 41, 31, and 27 t/ha of maize in sole maize, maize + cassava and maize + cassava + melon and 0.6 and 0.2 t/ha of sole melon and intercropped melon respectively. Nitrogen uptake by cassava and maize was highest under NSI, but fertilizer recommendation based on soil test gave the highest crop yield and monetary returns per unit of fertilizer used.
Judu V Ilavarasu
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.
KIM Jongwoo; LEE Hongjoo; PARK Sungjoo
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.
Aguayo-Albasini, José Luis; Flores-Pastor, Benito; Soria-Aledo, Víctor
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. Copyright © 2013 AEC. Published by Elsevier Espana. All rights reserved.
Rubén González Crespo
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.
Zeng, Wei; Zeng, An; Shang, Ming-Sheng; Zhang, Yi-Cheng
With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.
Scherr, Karen A; Fagerlin, Angela; Williamson, Lillie D; Davis, J Kelly; Fridman, Ilona; Atyeo, Natalie; Ubel, Peter A
Physicians' recommendations affect patients' treatment choices. However, most research relies on physicians' or patients' retrospective reports of recommendations, which offer a limited perspective and have limitations such as recall bias. To develop a reliable and valid method to measure the strength of physician recommendations using direct observation of clinical encounters. Clinical encounters (n = 257) were recorded as part of a larger study of prostate cancer decision making. We used an iterative process to create the 5-point Physician Recommendation Coding System (PhyReCS). To determine reliability, research assistants double-coded 50 transcripts. To establish content validity, we used 1-way analyses of variance to determine whether relative treatment recommendation scores differed as a function of which treatment patients received. To establish concurrent validity, we examined whether patients' perceived treatment recommendations matched our coded recommendations. The PhyReCS was highly reliable (Krippendorf's alpha = 0.89, 95% CI [0.86, 0.91]). The average relative treatment recommendation score for each treatment was higher for individuals who received that particular treatment. For example, the average relative surgery recommendation score was higher for individuals who received surgery versus radiation (mean difference = 0.98, SE = 0.18, P recommendations matched coded recommendations 81% of the time. The PhyReCS is a reliable and valid way to capture the strength of physician recommendations. We believe that the PhyReCS would be helpful for other researchers who wish to study physician recommendations, an important part of patient decision making. © The Author(s) 2016.
Beech, Anthony; Fisher, Dawn; Ward, Tony
Interviews with 28 sexual murderers were subjected to grounded theory analysis. Five implicit theories (ITs) were identified: dangerous world, male sex drive is uncontrollable, entitlement, women as sexual objects, and women as unknowable. These ITs were found to be identical to those identified in the literature as being present in rapists. The…
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.
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.
Full Text Available Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users’ recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.
Wang, Wei; Wang, Hongwei
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.
Pan, Rong; Xu, Guandong; Dolog, Peter
Tag as a useful metadata reflects the collaborative and conceptual features of documents in social collaborative annotation systems. In this paper, we propose a collaborative approach for expanding tag neighbors and investigate the spectral clustering algorithm to filter out noisy tag neighbors...... in order to get appropriate recommendation for users. The preliminary experiments have been conducted on MovieLens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach and naive tag neighbors expansion approach in terms of precision, and the result...... demonstrates that our approach could considerably improve the performance of recommendations....
Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali
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.
Basaran, Daniel; Ntoutsi, Eirini; Zimek, Arthur
A collection of datasets crawled from Amazon, “Amazon reviews”, is popular in the evaluation of recommendation systems. These datasets, however, contain redundancies (duplicated recommendations for variants of certain items). These redundancies went unnoticed in earlier use of these datasets...... and thus incurred to a certain extent wrong conclusions in the evaluation of algorithms tested on these datasets. We analyze the nature and amount of these redundancies and their impact on the evaluation of recommendation methods. While the general and obvious conclusion is that redundancies should...
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.
Full Text Available One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users’ past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1 we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2 we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user’s interests, thus improving prediction quality.
Williams, Craig H.
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.
A. H. Dong
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.
Chang, Tsung-Sheng; Hsiao, Wei-Hung
This study employs the perspective of social exchange theory and seeks to understand users' intentions to use social recommender systems (SRS) through three psychological factors: trust, shared values, and reputation. We use structural equation modeling to analyze 221 valid questionnaires. The results show that trust has a direct positive influence on the intention to use SRS, followed by shared values, whereas reputation has an indirect influence on SRS use. We further discuss specific recommendations concerning these factors for developing SRS.
KAYA, FİDAN; YILDIZ, GÜREL; KAVAK, ADNAN
In this paper, a recommendation system based on a mobile and web application is proposed for indoor decoration. The main contribution of this work is to apply two-stage filtering using linear matching and collaborative filtering to make recommendations. In the mobile application part, the image of the medium captured by a mobile phone is analyzed using color quantization methods, and these color analysis results along with other user-defined parameters such as height, width, and type of the p...
Evers, Stefan; Fiori, W; Brockmeyer, N; Arendt, G; Husstedt, I-W
HIV associated neuromanifestations are of growing importance in the in-patient treatment of HIV infected patients. In Germany, all in-patients have to be coded according to the ICD-10 classification and the German DRG-system. We present recommendations how to code the different primary and secondary neuromanifestations of HIV infection. These recommendations are based on the commentary of the German DRG procedures and are aimed to establish uniform coding of neuromanifestations.
Wolsiefer, Katie; Westfall, Jacob; Judd, Charles M
We explored the consequences of ignoring the sampling variation due to stimuli in the domain of implicit attitudes. A large literature in psycholinguistics has examined the statistical treatment of random stimulus materials, but the recommendations from this literature have not been applied to the social psychological literature on implicit attitudes. This is partly because of inherent complications in applying crossed random-effect models to some of the most common implicit attitude tasks, and partly because no work to date has demonstrated that random stimulus variation is in fact consequential in implicit attitude measurement. We addressed this problem by laying out statistically appropriate and practically feasible crossed random-effect models for three of the most commonly used implicit attitude measures-the Implicit Association Test, affect misattribution procedure, and evaluative priming task-and then applying these models to large datasets (average N = 3,206) that assess participants' implicit attitudes toward race, politics, and self-esteem. We showed that the test statistics from the traditional analyses are substantially (about 60 %) inflated relative to the more-appropriate analyses that incorporate stimulus variation. Because all three tasks used the same stimulus words and faces, we could also meaningfully compare the relative contributions of stimulus variation across the tasks. In an appendix, we give syntax in R, SAS, and SPSS for fitting the recommended crossed random-effects models to data from all three tasks, as well as instructions on how to structure the data file.
Full Text Available The following article presents an usability study of a Mash-up Personal Learning Environment called ReMashed that recommends items from the emerging information of a Learning Network. In ReMashed users can specify certain Web 2.0 services and combine them in a Mash-Up Personal Learning Environment. The users can rate information from an emerging amount of Web 2.0 information of a Learning Network and train a recommender system for their particular needs. In total 49 participants from 8 different countries registered to evaluate the ReMashed system. The participants contributed Web 2.0 contents and used the recommender system for one month. The evaluation was concluded with an online questionnaire where most of the participants were positive about the ReMashed system and offered helpful ideas for future developments.
Fazeli, Soude; Drachsler, Hendrik; Bitter-Rijpkema, Marlies; Brouns, Francis; Van der Vegt, Wim; Sloep, Peter
Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user
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)
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.
Berradre-Sáenz, Belén; Royo-Bordonada, Miguel Ángel; Bosqued, María José; Moya, María Ángeles; López, Lázaro
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.
Verbert, K.; Manouselis, N.; Ochoa, X.; Wolpers, M.; Drachsler, H.; Bosnic, I.; Duval, E.
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…
Fenn, B.; Lennon, J.
There has been increasing interest over the past few years in systems that help users exchange recommendations about World Wide Web documents. Programs have ranged from those that rely totally on user pre-selection, to others that are based on artificial intelligence. This paper proposes a system that falls between these two extremes, providing…
Chien, Tzu-Chao; Chen, Zhi-Hong; Chan, Tak-Wai
This study explored the behavior of students who used a book recommendation system, specifically the My-Bookstore system, over a five semester period. This study addressed two main research questions, the first being related to "the most frequent behaviors and behavioral patterns." The results showed that "visiting" behavior…
Evale, Digna S.
Aim/Purpose: This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background: It utilized five-year historical data to find patterns for predicting student performance in Java Programming to generate…
In this dissertation I examine the design, construction and implementation of an online blog ratings and user recommender system for the Claremont Conversation Online (CCO). In line with constructivist learning models and practical information systems (IS) design, I implemented a blog ratings system (a system that can be extended to allow for…
Bahramian, Z.; Abbaspour, R. Ali; Claramunt, C.
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.
Ramezani, Mohsen; Yaghmaee, Farzin
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.
Full Text Available Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item’s tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems.
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.
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.
P.J. van der Houwen; W.A. van der Veen
textabstractWe apply a Runge-Kutta-based waveform relaxation method to initial-value problems for implicit differential equations. In the implementation of such methods, a sequence of nonlinear systems has to be solved iteratively in each step of the integration process. The size of these systems
Paula Andrea RODRÍGUEZ MARÍN
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.
Nielsen, Anne Maj
The field of mindfulness and meditation has met growing interest in the western world during the last decades. Mindfulness aims to develop a friendly, accepting and mindful awareness in the present moment. Critiques have argued that this aim is deployed in a new kind of management technology where...... 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...
Ling, W; Chia, R C; Fang, L
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.
Roberts, Malcolm; Bowman, John C.
Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly. This is accomplished by processing and discarding data as it is generated, allowing work memory to be reused, for greater data locality and performance. A multithreaded implementation of implicit dealiasing that accepts an arbitrary number of input and output vectors and a general multiplication operator is presented, along with an improved one-dimensional Hermitian convolution that avoids the loop dependency inherent in previous work. An alternate data format that can accommodate a Nyquist mode and enhance cache efficiency is also proposed.
Full Text Available The present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system for urban sightseeing spots in order to support individual, as well as group sightseeing activities while taking into consideration the user’s needs, which can change according to the circumstances (each user’s important conditions and sightseeing unit. The system was developed by integrating Web-GIS (Geographic Information Systems, the pairing system, the evaluation system, as well as the recommendation system into a single system, and it was also connected with external SNS (Social Networking Services: Twitter and Facebook. Additionally, the system was operated for four weeks in the central part of Yokohama City in Kanagawa Prefecture, Japan, and the total number of users was 52. Based on the results of the web questionnaire survey, the usefulness of the system when sightseeing was high, and the recommendation function of sightseeing spots, which is an original function, has received mainly good ratings. From the results of the access analysis of users’ log data, it is evident that the system has been used by different types of devices, just as it was designed for, and that the system has been used according to the purpose of the present study, which is to support the sightseeing activities of users.
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.
Flusser, Jan; Kautský, J.; Šroubek, Filip
Roč. 86, č. 1 (2010), s. 72-86 ISSN 0920-5691 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/1593 Institutional research plan: CEZ:AV0Z10750506 Keywords : Implicit invariants * Orthogonal polynomials * Polynomial image deformation Subject RIV: BD - Theory of Information Impact factor: 4.930, year: 2010 http://library.utia.cas.cz/separaty/2009/ZOI/flusser-0329394.pdf
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.
Takamura, Eduardo; Mangum, Kevin
. 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).
Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang
This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.
Li, Y.; Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; Finch, C. J.; McGibbney, L. J.
Earth observations are produced in a fast velocity through real time sensors, reaching tera- to peta- bytes of geospatial data daily. Discovering and accessing the right data from the massive geospatial data is like finding needle in the haystack. To help researchers find the right data for study and decision support, quite a lot of research focusing on improving search performance have been proposed including recommendation algorithm. However, few papers have discussed the way to implement a recommendation algorithm in geospatial data retrieval system. In order to address this problem, we propose a recommendation engine to improve discovering relevant geospatial data by mining and utilizing metadata and user behavior data: 1) metadata based recommendation considers the correlation of each attribute (i.e., spatiotemporal, categorical, and ordinal) to data to be found. In particular, phrase extraction method is used to improve the accuracy of the description similarity; 2) user behavior data are utilized to predict the interest of a user through collaborative filtering; 3) an integration method is designed to combine the results of the above two methods to achieve better recommendation Experiments show that in the hybrid recommendation list, the all the precisions are larger than 0.8 from position 1 to 10.
This recommendation contains the detailed specification of the logic required to carry out the Command Operations Procedures of the Transfer Layer. The Recommendation for Telecommand--Part 2, Data Routing Service contains the standard data structures and data communication procedures used by the intermediate telecommand system layers (the Transfer and Segmentation Layers). In particular, it contains a brief description of the Command Operations Procedures (COP) within the Transfer Layer. This recommendation contains the detailed definition of the COP's in the form of state tables, along with definitions of the terms used. It is assumed that the reader of this document is familiar with the data structures and terminology of part 2. In case of conflict between the description of the COP's in part 2 and in this recommendation, the definition in this recommendation will take precedence. In particular, this document supersedes section 188.8.131.52 through 184.108.40.206 of part 2.
Jensen, Christian D.
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...... of an article based on feedback from other Wikipedia users. The WRS calculates a personalised rating for any Wikipedia article based on feedback (recommendations) provided by other Wikipedia users. As part of this process, WRS users are expected to provide their own feedback about the quality of Wikipedia...
Neely, Alice N; Weber, Joan M; Daviau, Patricia; MacGregor, Alastair; Miranda, Carlos; Nell, Marie; Bush, Patricia; Lighter, Donald
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.
Full Text Available Recommender systems suggest proper items to customers based on their preferences and needs. Needed time to search is reduced and the quality of customer’s choice is increased using recommender systems. The context information like time, location and user behaviors can enhance the quality of recommendations and customer satisfication in such systems. In this paper a context aware recommender system is designed and implemented in android smart phones to help customers select mobile phones. The system removes ineffective criteria on user’s purcheses using customer mobile phones’ sensor data. Then creates analytic hierarchy processing tree and computes weights. Finally the recommender system recommends proper mobile phone to user. The system selects and recommends suitable phones using combination of elimination method and analytic hierarchy processing (AHP. The context aware recommender system is used by mobile phone customers to assess recomendation satisfication and user interface design satisfication. In addition a traditional non-context aware recommender system is used by users to compare the recommendation results in two different systems. The article concludes that using context information can improve the recommendation quality and user satisfication. Because of decreasing criteria and pair-wised comparisions, the user interface design satisfication improves a little too.
This report presents recommendations for minimum DSRC device communication performance and security : requirements to ensure effective operation of the DSRC system. The team identified recommended DSRC : communications requirements aligned to use cas...
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.
Fernandez-Luque, Luis; Karlsen, Randi; Vognild, Lars K
The use of computers in health education started more than a decade ago, mainly for tailoring health educational resources. Nowadays, many of the computer-tailoring health education systems are using the Internet for delivering different types of health education. Traditionally, these systems are designed for a specific health problem, with a predefined library of educational resources. These systems do not take advantage of the increasing amount of educational resources available on the Internet. One of the reasons is that the high availability of content is making it more difficult to find the relevant one. The problem of information overload has been addressed for many years in the field of recommender systems. This paper is focused on the challenges and opportunities of merging recommender systems with personalized health education. It also discusses the usage of social networks and semantic technologies within this approach.
Full Text Available In this paper, we propose a cooking recipe recommendation system which runs on a consumer smartphone as an interactive mobile application. The proposed system employs real-time visual object recognition of food ingredients, and recommends cooking recipes related to the recognized food ingredients. Because of visual recognition, by only pointing a built-in camera on a smartphone to food ingredients, a user can get to know a related cooking recipes instantly. The objective of the proposed system is to assist people who cook to decide a cooking recipe at grocery stores or at a kitchen. In the current implementation, the system can recognize 30 kinds of food ingredient in 0.15 seconds, and it has achieved the 83.93% recognition rate within the top six candidates. By the user study, we confirmed the effectiveness of the proposed system.
Kaufman, Scott Barry; Deyoung, Colin G; Gray, Jeremy R; Jiménez, Luis; Brown, Jamie; Mackintosh, Nicholas
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. 2010 Elsevier B.V. All rights reserved.
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.
Xia, Zhengyou; Xu, Shengwu; Liu, Ningzhong; Zhao, Zhengkang
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.
Full Text Available It is well documented that explicit memory (e.g., recognition declines with age. In contrast, many argue that implicit memory (e.g., priming is preserved in healthy aging. For example, priming on tasks such as perceptual identification is often not statistically different in groups of young and older adults. Such observations are commonly taken as evidence for distinct explicit and implicit learning/memory systems. In this article we discuss several lines of evidence that challenge this view. We describe how patterns of differential age-related decline may arise from differences in the ways in which the two forms of memory are commonly measured, and review recent research suggesting that under improved measurement methods, implicit memory is not age-invariant. Formal computational models are of considerable utility in revealing the nature of underlying systems. We report the results of applying single and multiple-systems models to data on age effects in implicit and explicit memory. Model comparison clearly favours the single-system view. Implications for the memory systems debate are discussed.
Ward, Emma V; Berry, Christopher J; Shanks, David R
It is well-documented that explicit memory (e.g., recognition) declines with age. In contrast, many argue that implicit memory (e.g., priming) is preserved in healthy aging. For example, priming on tasks such as perceptual identification is often not statistically different in groups of young and older adults. Such observations are commonly taken as evidence for distinct explicit and implicit learning/memory systems. In this article we discuss several lines of evidence that challenge this view. We describe how patterns of differential age-related decline may arise from differences in the ways in which the two forms of memory are commonly measured, and review recent research suggesting that under improved measurement methods, implicit memory is not age-invariant. Formal computational models are of considerable utility in revealing the nature of underlying systems. We report the results of applying single and multiple-systems models to data on age effects in implicit and explicit memory. Model comparison clearly favors the single-system view. Implications for the memory systems debate are discussed.
Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang
We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user's N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user's N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues.
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.
Full Text Available We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user’s N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user’s N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues.
Linda S. Heath; Sarah M. Anderson; Marla R. Emery; Jeffrey A. Hicke; Jeremy Littell; Alan Lucier; Jeffrey G. Masek; David L. Peterson; Richard Pouyat; Kevin M. Potter; Guy Robertson; Jinelle Sperry; Andrzej Bytnerowicz; Sarah Jovan; Miranda H. Mockrin; Robert Musselman; Bethany K. Schulz; Robert J. Smith; Susan I. Stewart
The Third National Climate Assessment (NCA) process for the United States focused in part on developing a system of indicators to communicate key aspects of the physical climate, climate impacts, vulnerabilities, and preparedness to inform decisionmakers and the public. Initially, 13 active teams were formed to recommend indicators in a range of categories, including...
Full Text Available Peninsula University of Technology, 10 September 2013 Selection and provisioning of services in a cloud using recommender systems approach for SMME S. Manqele1, N.Dlodlo2, P.Mvelase3, M. Dlodlo4 , S.S. Xulu5, M. Adigun6 1, 2, 3 CSIR – Meraka...
Lin, Y.; Jessurun, J.; Vries, de B.; Timmermans, H.J.P.
This paper presents the practices of a research aiming at the design of a context-aware recommendation system that promotes the adoption of a healthy and active lifestyle. A Smartphone application that provides personalized and contextualized advice on physical activities was developed. The goal of
Liu, H.; Hu, J.; Rauterberg, G.W.M.; Spink, A.J.; Grieco, O.E.; Krips, L.W.S.; Loijens, L.P.J.J.; Noldus, xx; Zimmerman, P.H.
Travel by air, especially long distance, the enclosed environment of the aircraft cabin causes discomfort and even stress to flight passengers. In this paper, we present a new heart rate controlled music recommendation system. Heart rate is used as a stress indicator. If the user is stressed and
Conforti, R.; Leoni, de M.; La Rosa, M.; Aalst, van der W.M.P.; Hofstede, ter A.H.M.
This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a
Conforti, R.; Leoni, de M.; La Rosa, M.; Aalst, van der W.M.P.; Hofstede, ter A.H.M.
This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a
Tawfik, Andrew A.; Alhoori, Hamed; Keene, Charles Wayne; Bailey, Christian; Hogan, Maureen
In case library learning environments, learners are presented with an array of narratives that can be used to guide their problem solving. However, according to theorists, learners struggle to identify and retrieve the optimal case to solve a new problem. Given the challenges novice face during case retrieval, recommender systems can be embedded…
Sappelli, M.; Verberne, S.; Kraaij, W.
In this article we evaluate context-aware recommendation systems for information re-finding by knowledge workers. We identify 4 criteria that are relevant for evaluating the quality of knowledge worker support: context relevance, document relevance, prediction of user action, and diversity of the
Dwivedi, Pragya; Bharadwaj, Kamal K.
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…
Voges, Mickie; And Others
A modular program is recommended for automation of the clippings file of "The Daily Texan" (student newspaper of the University of Texas at Austin). The proposed system will lead ultimately to on-line storage of the index, on-line storage of local, staff-written news stories from the previous twenty-four months, micrographic storage for backup and…
Ionization chamber smoke detectors (ICSDs) utilising a radioactive substance as the source of ionization are used to detect the presence of smoke and hence give early warning of a fire. These recommendations are intended to ensure that the use of ICSDs incorporating radium-226 and americium-241 in commercial/industrial fire protection systems does not give rise to any unnecessary radiation exposure
Tanaka, M; Nakazono, S; Matsuno, H; Tsujimoto, H; Kitamura, Y; Miyano, S
We have implemented a system for assisting experts in selecting MEDLINE records for database construction purposes. This system has two specific features: The first is a learning mechanism which extracts characteristics in the abstracts of MEDLINE records of interest as patterns. These patterns reflect selection decisions by experts and are used for screening the records. The second is a keyword recommendation system which assists and supplements experts' knowledge in unexpected cases. Combined with a conventional keyword-based information retrieval system, this system may provide an efficient and comfortable environment for MEDLINE record selection by experts. Some computational experiments are provided to prove that this idea is useful.
Magdon-Ismail, Zainab; Benesch, Curtis; Cushman, Jeremy T; Brissette, Ian; Southerland, Andrew M; Brandler, Ethan S; Sozener, Cemal B; Flor, Sue; Hemmitt, Roseanne; Wales, Kathleen; Parrigan, Krystal; Levine, Steven R
The American Heart Association/American Stroke Association and Department of Health Stroke Coverdell Program convened a stakeholder meeting in upstate NY to develop recommendations to enhance stroke systems for acute large vessel occlusion. Prehospital, hospital, and Department of Health leadership were invited (n=157). Participants provided goals/concerns and developed recommendations for prehospital triage and interfacility transport, rating each using a 3-level impact (A [high], B, and C [low]) and implementation feasibility (1 [high], 2, and 3 [low]) scale. Six weeks later, participants finalized recommendations. Seventy-one stakeholders (45% of invitees) attended. Six themes around goals/concerns emerged: (1) emergency medical services capacity, (2) validated prehospital screening tools, (3) facility capability, (4) triage/transport guidelines, (5) data capture/feedback tools, and (6) facility competition. In response, high-impact (level A) prehospital recommendations, stratified by implementation feasibility, were (1) use of online medical control for triage (6%); (2) regional transportation strategy (31%), standardized emergency medical services checklists (18%), quality metrics (14%), standardized prehospital screening tools (13%), and feedback for performance improvement (7%); and (3) smartphone application algorithm for screening/decision-making (6%) and ambulance-based telemedicine (6%). Level A interfacility transfer recommendations were (1) standardized transfer process (32%)/timing goals (16%)/regionalized systems (11%), performance metrics (11%), image sharing capabilities (7%); (2) provider education (9%) and stroke toolbox (5%); and (3) interfacility telemedicine (7%) and feedback (2%). The methods used and recommendations generated provide models for stroke system enhancement. Implementation may vary based on geographic need/capacity and be contingent on establishing standard care practices. Further research is needed to establish optimal
Guo, Li; Jin, Bo; Yao, Cuili; Yang, Haoyu; Huang, Degen; Wang, Fei
Key opinion leaders (KOLs) are people who can influence public opinion on a certain subject matter. In the field of medical and health informatics, it is critical to identify KOLs on various disease conditions. However, there have been very few studies on this topic. We aimed to develop a recommender system for identifying KOLs for any specific disease with health care data mining. We exploited an unsupervised aggregation approach for integrating various ranking features to identify doctors who have the potential to be KOLs on a range of diseases. We introduce the design, implementation, and deployment details of the recommender system. This system collects the professional footprints of doctors, such as papers in scientific journals, presentation activities, patient advocacy, and media exposure, and uses them as ranking features to identify KOLs. We collected the information of 2,381,750 doctors in China from 3,657,797 medical journal papers they published, together with their profiles, academic publications, and funding. The empirical results demonstrated that our system outperformed several benchmark systems by a significant margin. Moreover, we conducted a case study in a real-world system to verify the applicability of our proposed method. Our results show that doctors' profiles and their academic publications are key data sources for identifying KOLs in the field of medical and health informatics. Moreover, we deployed the recommender system and applied the data service to a recommender system of the China-based Internet technology company NetEase. Patients can obtain authority ranking lists of doctors with this system on any given disease.
Shi, Xiaoyu; Shang, Ming-Sheng; Luo, Xin; Khushnood, Abbas; Li, Jian
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.
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.
Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet
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.
Leng, Yan; Rudolph, Larry; Pentland, Alex 'Sandy'; Zhao, Jinhua; Koutsopolous, Haris N.
Growth in leisure travel has become increasingly significant economically, socially, and environmentally. However, flexible but uncoordinated travel behaviors exacerbate traffic congestion. Mobile phone records not only reveal human mobility patterns, but also enable us to manage travel demand for system efficiency. In this paper, we propose a location recommendation system that infers personal preferences while accounting for constraints imposed by road capacity in order to manage travel dem...
Smith, J David; Zakrzewski, Alexandria C; Herberger, Eric R; Boomer, Joseph; Roeder, Jessica L; Ashby, F Gregory; Church, Barbara A
Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization.
Devaraju, A.; Davy, R.; Hogan, D.
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.
Zhou, Wei; Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian
Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user's credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.
Zanitti, Michele; Kosta, Sokol; Sørensen, Jannick Kirk
Recommender systems (RS) have seen widespread adoption across the Internet. However, by emphasizing personalization through the optimization of accuracy-focused metrics, over-personalization may emerge, with negative effects on the user experience. A countermeasure to the problem is to diversify...... recommendations. In this paper, we present a solution that addresses the problem in the context of a movie application domain. The solution enhances diversity on four related dimensions, namely global coverage, local coverage, novelty, and redundancy. The proposed solution is designed to diversify users profiles......, modeled on categorical preferences, within the same group in the recommendation filtering. We evaluate our approach on the Movielens dataset and show that our algorithm yields better results compared to random selection distant neighbors and performs comparably to one of the current state of the art...
Agapito, Giuseppe; Simeoni, Mariadelina; Calabrese, Barbara; Caré, Ilaria; Lamprinoudi, Theodora; Guzzi, Pietro H; Pujia, Arturo; Fuiano, Giorgio; Cannataro, Mario
Use of mobile and web-based applications for diet and weight management is currently increasing. However, the impact of known apps on clinical outcomes is not well-characterized so far. Moreover, availability of food recommender systems providing high quality nutritional advices to both healthy and diet-related chronic diseases users is very limited. In addition, the potentiality of nutraceutical properties of typical regional foods for improving app utility has not been exerted to this end. We present DIETOS, a recommender system for the adaptive delivery of nutrition contents to improve the quality of life of both healthy subjects and patients with diet-related chronic diseases. DIETOS provides highly specialized nutritional advices in different health conditions. DIETOS was projected to provide users with health profile and individual nutritional recommendation. Health profiling was based on user answers to dynamic real-time medical questionnaires. Furthermore, DIETOS contains catalogs of typical foods from Calabria, a southern Italian region. Several Calabrian foods have been inserted because of their nutraceutical properties widely reported in several quality studies. DIETOS includes some well known methods for user profiling (overlay profiling) and content adaptation (content selection) coming from general purpose adaptive web systems. DIETOS has been validated for usability for both patients and specialists and for assessing the correctness of the profiling and recommendation, by enrolling 20 chronic kidney disease (CKD) patients at the Department of Nephrology and Dialysis, University Hospital, Catanzaro (Italy) and 20 age-matched healthy controls. Recruited subjects were invited to register to DIETOS and answer to medical questions to determine their health status. Based on our results, DIETOS has high specificity and sensitivity, allowing to determine a medical-controlled user's health profile and to perform a fine-grained recommendation that is better
He, Xu; Liu, Bin; Chen, Kejia
The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error.
Full Text Available Nowadays, organisations are constantly looking for methods to improve their efficiency and to guarantee them a competitive advantage, sustainable profitable growth and ability to survive in a turbulent environment. An increasing popularity of implementation of certified management systems has been noticed. The most often integrated management system are eg. ISO 9001, ISO 14001, ISO 18001. The paper presents the conditions for the implementation of an integrated management system (IMS, the characteristics of the most integrated management systems, the advantages and disadvantages of integration and the difficulties in the implementation of IMS and recommendations regarding the effectiveness of the integration of IMS.
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.
Kashima, Tomoko; Matsumoto, Shimpei; Ishii, Hiroaki
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.
Li, S. G.; Shi, L.
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.
Yu, Penghua; Lin, Lanfen; Wang, Jing
Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.
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.
Iwasaki, Wataru; Yamamoto, Yasunori; Takagi, Toshihisa
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.
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.
Howell, Ann-Marie; Burns, Elaine M; Hull, Louise; Mayer, Erik; Sevdalis, Nick; Darzi, Ara
Patient safety incident reporting systems (PSRS) have been established for over a decade, but uncertainty remains regarding the role that they can and ought to play in quantifying healthcare-related harm and improving care. To establish international, expert consensus on the purpose of PSRS regarding monitoring and learning from incidents and developing recommendations for their future role. After a scoping review of the literature, semi-structured interviews with experts in PSRS were conducted. Based on these findings, a survey-based questionnaire was developed and subsequently completed by a larger expert panel. Using a Delphi approach, consensus was reached regarding the ideal role of PSRSs. Recommendations for best practice were devised. Forty recommendations emerged from the Delphi procedure on the role and use of PSRS. Experts agreed reporting system should not be used as an epidemiological tool to monitor the rate of harm over time or to appraise the relative safety of hospitals. They agreed reporting is a valuable mechanism for identifying organisational safety needs. The benefit of a national system was clear with respect to medication error, device failures, hospital-acquired infections and never events as these problems often require solutions at a national level. Experts recommended training for senior healthcare professionals in incident investigation. Consensus recommendation was for hospitals to take responsibility for creating safety solutions locally that could be shared nationally. We obtained reasonable consensus among experts on aims and specifications of PSRS. This information can be used to reflect on existing and future PSRS, and their role within the wider patient safety landscape. The role of PSRS as instruments for learning needs to be elaborated and developed further internationally. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
Wu, Jun; Su, Zhou; Wang, Shen; Li, Jianhua
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.
Full Text Available 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.
Woods, Susan Swartz; Jaén, Carlos Roberto
Health professionals play an important role in addressing patient tobacco use in clinical settings. While there is clear evidence that identifying tobacco use and assisting smokers in quitting affects outcomes, challenges to improve routine, clinician-delivered tobacco intervention persist. The Consumer Demand Initiative has identified simple design principles to increase consumers' use of proven tobacco treatments. Applying these design strategies to activities across the healthcare system, we articulate ten recommendations that can be implemented in the context of most clinical systems where most clinicians work. The recommendations are: (1) reframe the definition of success, (2) portray proven treatments as the best care, (3) redesign the 5A's of tobacco intervention, (4) be ready to deliver the right treatment at the right time, (5) move tobacco from the social history to the problem list, (6) use words as therapy and language that makes sense, (7) fit tobacco treatment into clinical team workflows, (8) embed tobacco treatment into health information technology, (9) make every encounter an opportunity to intervene, and (10) end social disparities for tobacco users. Clinical systems need to change to improve tobacco treatment implementation. The consumer- and clinician-centered recommendations provide a roadmap that focuses on increasing clinician performance through greater understanding of the clinician's role in helping tobacco users, highlighting the value of evidence-based tobacco treatments, employing shared decision-making skills, and integrating routine tobacco treatment into clinical system routines. Published by Elsevier Inc.
Wu, Jun; Su, Zhou; Li, Jianhua
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. PMID:28758943
Various main propulsion system configurations of an advanced OTV are evaluated with respect to the probability of nonindependent failures, i.e., engine failures that disable the entire main propulsion system. Analysis of the life-cycle cost (LCC) indicates that LCC is sensitive to the main propulsion system reliability, vehicle dry weight, and propellant cost; it is relatively insensitive to the number of missions/overhaul, failures per mission, and EVA and IVA cost. In conclusion, two or three engines are recommended in view of their highest reliability, minimum life-cycle cost, and fail operational/fail safe capability.
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.
Shin, Il-Hyung; Cha, Jaepyeong; Cheon, Gyeong Woo; Lee, Choonghee; Lee, Seung Yup; Yoon, Hyung-Jin; Kim, Hee Chan
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.
Terán Tamayo, Luis Fernando
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
Junping Dong; Qingyu Xiong; Junhao Wen; Peng Li
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-functi...
Maryam Haji Shah Karam
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.
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.
Schnack, D.D.; Barnes, D.C.; Mikic, Z.; Harned, D.S.; Caramana, E.J.
A semi-implicit algorithm for the solution of the nonlinear, three-dimensional, resistive MHD equations in cylindrical geometry is presented. The specific model assumes uniform density and pressure, although this is not a restriction of the method. The spatial approximation employs finite differences in the radial coordinate, and the pseudo-spectral algorithm in the periodic poloidal and axial coordinates. A leapfrog algorithm is used to advance wave-like terms; advective terms are treated with a simple predictor--corrector method. The semi-implicit term is introduced as a simple modification to the momentum equation. Dissipation is treated implicitly. The resulting algorithm is unconditionally stable with respect to normal modes. A general discussion of the semi-implicit method is given, and specific forms of the semi-implicit operator are compared in physically relevant test cases. Long-time simulations are presented. copyright 1987 Academic Press, Inc
User interest profile presents items that the users are interested in. Typically those items can be listed or grouped. Listing is good but it does not possess interests at different abstraction levels - the higher-level interests are more general, while the lower-level ones are more specific. Furthermore, more general interests, in some sense, correspond to longer-term interests, while more specific interests correspond to shorter-term interests. This hierarchical user interest profile has obvious advantages: specifying user's specific interests and general interests and representing their relationships. Current user interest profile structures mostly do not use implicit method, nor use an appropriate clustering algorithm especially for conceptually hierarchical structures. This research studies building a hierarchical user interest profile (HUIP) and the hierarchical divisive algorithm (HDC). Several users visit hundreds of web pages and each page is recorded in each users profile. These web pages are used t...
Revilla Muñoz, Olga; Alpiste Penalba, Francisco; Fernández Sánchez, Joaquín
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.
Klimkowicz-Mrowiec, Aleksandra; Slowik, Agnieszka; Krzywoszanski, Lukasz; Herzog-Krzywoszanska, Radosława; Szczudlik, Andrzej
Consistent evidence from human and experimental animals studies indicates that memory is organized into two relatively independent systems with different functions and brain mechanisms. The explicit memory system, dependent on the hippocampus and adjacent medial temporal lobe structures, refers to conscious knowledge acquisition and intentional recollection of previous experiences. The implicit memory system, dependent on the striatum, refers to learning of complex information without awareness or intention. The functioning of implicit memory can be observed in progressive, gradual improvement across many trials in performance on implicit learning tasks. The influence of explicit memory on implicit memory has not been precisely identified yet. According to data from some studies, explicit memory seems to exhibit no influence on implicit memory,whereas the other studies indicate that explicit memory may inhibit or facilitate implicit memory. The analysis of performance on implicit learning tasks in patients with different severity of explicit memory impairment due to Alzheimer's disease allows one to identify the potential influence of the explicit memory system on the implicit memory system. 51 patients with explicit memory impairment due to Alzheimer's disease (AD) and 36 healthy controls were tested. Explicit memory was examined by means of a battery of neuropsychological tests. Implicit habit learning was examined on probabilistic classification task (weather prediction task). Patients with moderate explicit memory impairment performed the implicit task significantly better than those with mild AD and controls. Results of our study support the hypothesis of competition between the implicit and explicit memory systems in humans.
Ben Hammouda, Chiheb
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.
Okoth, P.F.; Wamae, D.K.
A GIS database was established for fertiliser recommendation domains in Kisii District by using FURP fertiliser trial results, KSS soils data and MDBP climatic data. These are manipulated in ESRI's (Personal Computer Environmental Systems Research Institute) ARCINFO and ARCVIEW softwares. The extrapolations were only done for the long rains season (March- August) with three to four years data. GIS technology was used to cluster fertiliser recommendation domains as a geographical area expressed in terms of variation over space and not limited to the site of experiment where a certain agronomic or economic fertiliser recommendation was made. The extrapolation over space was found to be more representative for any recommendation, the result being digital maps describing each area in the geographical space. From the results of the extrapolations, approximately 38,255 ha of the district require zero Nitrogen (N) fertilisation while 94,330 ha requires 75 kg ha -1 Nitrogen fertilisation during the (March-August) long rains. The extrapolation was made difficult since no direct relationships could be established to occur between the available-N, % Carbon (C) or any of the other soil properties with the obtained yields. Decision rules were however developed based on % C which was the soil variable with values closest to the obtained yields. 3% organic carbon was found to be the boundary between 0 application and 75 kg-N application. GIS techniques made it possible to model and extrapolates the results using the available data. The extrapolations still need to be verified with more ground data from fertiliser trials. Data gaps in the soil map left some soil mapping units with no recommendations. Elevation was observed to influence yields and it should be included in future extrapolation by clustering digital elevation models with rainfall data in a spatial model at the district scale
Shang, Ming-Sheng; Zhang, Zi-Ke
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.
John W. Coffey
Full Text Available The literature on intelligent or adaptive tutoring systems generally has a focus on how to determine what resources to present to students as they make their way through a course of study. The idea of multi-faceted student modeling is that a variety of measures, both academic and non-academic, might be represented in student models in service of a broader educational context. This paper contains a framework for a multi-faceted, educational, knowledge-based recommender system, including a basic set of descriptors that the model contains, and a taxonomy of inferences that might be made over such models.
Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq
Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.
Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca
With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.
Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq
Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim’ based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks. PMID:26222882
Zhou, Tao; Medo, Matúš; Cimini, Giulio; Zhang, Zi-Ke; Zhang, Yi-Cheng
The study of the organization of social networks is important for the 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.
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.
Achakulvisut, Titipat; Acuna, Daniel E; Ruangrong, Tulakan; Kording, Konrad
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.
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.
The workshop included an opening session, seven sessions with participant presentations followed by short discussions, and a facilitated discussion session. The contributions presented were devoted to discussions of national post-Fukushima regulatory programme developments, methods to determine allowable coping time for electric power recovery, electric power system simulation methods development and benchmarking efforts, analysis of component capability, and approaches to facilitate electric power system recovery from extended loss of AC power. The following conclusions and recommendations are made based on workshop presentations, discussions during particular sessions, and facilitated discussions: - Based upon the panel discussions at the end of the workshop, a majority of the participants suggested the need for continuing efforts after the ROBELSYS workshop and particularly the importance of launching a more permanent international working group on modeling tools and methods related to nuclear power plant electrical power system studies. The working group would be modelled on WGRISK. (It is recognized that creating such a permanent working group would require a multi-year commitment of CSNI and the participants). - It will be very beneficial to continue international information sharing of the following items, eventually leading to development of suitable international electrical standards: System and component requirements for addressing beyond design basis external events; Recommended practice for incorporating diversity in the onsite electrical power system; Recommended practice for relaxing electric power protection features used in emergency situations (assuring margin against spurious electrical shutdowns); Recommended practice for qualification requirements for existing systems and portable components used to cope with AC station blackout. - There is a need for further development and improvements in the analysis and simulation of the following
Sadasivam, Rajani Shankar; Cutrona, Sarah L; Kinney, Rebecca L; Marlin, Benjamin M; Mazor, Kathleen M; Lemon, Stephenie C; Houston, Thomas K
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.
Hors-Fraile, Santiago; Rivera-Romero, Octavio; Schneider, Francine; Fernandez-Luque, Luis; Luna-Perejon, Francisco; Civit-Balcells, Anton; de Vries, Hein
Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population. This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, "Ensure healthy lives and promote well-being for all at all ages"), and to suggest possible reasons for these gaps as well as to propose some solutions. We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-language journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects-the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects-using a new multidisciplinary taxonomy. Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and behavior change theories were not observed in
Hulstijn, J.H.; Rebuschat, P.
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
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
Qazanfari, K.; Youssef, A.; Keane, K.; Nelson, J.
With the recent increase in data online, discovering meaningful opportunities can be time-consuming and complicated for many individuals. To overcome this data overload challenge, we present a novel text-content-based recommender system as a valuable tool to predict user interests. To that end, we develop a specific procedure to create user models and item feature-vectors, where items are described in free text. The user model is generated by soliciting from a user a few keywords and expanding those keywords into a list of weighted near-synonyms. The item feature-vectors are generated from the textual descriptions of the items, using modified tf-idf values of the users’ keywords and their near-synonyms. Once the users are modeled and the items are abstracted into feature vectors, the system returns the maximum-similarity items as recommendations to that user. Our experimental evaluation shows that our method of creating the user models and item feature-vectors resulted in higher precision and accuracy in comparison to well-known feature-vector-generating methods like Glove and Word2Vec. It also shows that stemming and the use of a modified version of tf-idf increase the accuracy and precision by 2% and 3%, respectively, compared to non-stemming and the standard tf-idf definition. Moreover, the evaluation results show that updating the user model from usage histories improves the precision and accuracy of the system. This recommender system has been developed as part of the Agnes application, which runs on iOS and Android platforms and is accessible through the Agnes website.
Cerón-Rios, Gineth; López, Diego M; Blobel, Bernd
Recommender systems (RS) are useful tools for filtering and sorting items and information for users. There is a wide diversity of approaches that help creating personalized recommendations. Context-aware recommender systems (CARS) are a kind of RS which provide adaptation capabilities to the user's environment, e.g., by sensing data through wearable devices or other biomedical sensors. In healthcare and wellbeing, CARS can support health promotion and health education, considering that each individual requires tailored intervention programs. Our research aims at proposing a context-aware mobile recommender system for the promotion of healthy habits. The system is adapted to the user's needs, his/her health information, interests, time, location and lifestyles. In this paper, the CARS computational architecture and the user and context models of health promotion are presented, which were used to implement and test a prototype recommender system.
Wang, Ximeng; Liu, Yun; Zhang, Guangquan; Xiong, Fei; Lu, Jie
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.