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

CERN Document Server

Gedikli, Fatih

2013-01-01

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

Recommender systems in knowledge-mining

Science.gov (United States)

Volna, Eva

2017-07-01

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

Mining and representing recommendations in actively evolving recommender systems

DEFF Research Database (Denmark)

Assent, Ira

2010-01-01

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....

Classification of Recommender Expertise in the Wikipedia Recommender System

DEFF Research Database (Denmark)

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

2011-01-01

to the quality of articles. The Wikipedia Recommender System (WRS) was developed to help users determine the credibility of articles based on feedback from other Wikipedia users. The WRS implements a collaborative filtering system with trust metrics, i.e., it provides a rating of articles which emphasizes...... 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...

Classification of Recommender Expertise in the Wikipedia Recommender System

DEFF Research Database (Denmark)

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

2011-01-01

to the quality of articles. The Wikipedia Recommender System (WRS) was developed to help users determine the credibility of articles based on feedback from other Wikipedia users. The WRS implements a collaborative filtering system with trust metrics, i.e., it provides a rating of articles "which emphasizes...... 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...

A Flexible Electronic Commerce Recommendation System

Science.gov (United States)

Gong, Songjie

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

Recommender Systems in Commercial Use

OpenAIRE

Aldrich, Susan E.

2011-01-01

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...

Recommendation systems in software engineering

CERN Document Server

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

2014-01-01

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

Recommender Systems for Learning

CERN Document Server

Manouselis, Nikos; Verbert, Katrien; Duval, Erik

2013-01-01

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

Introduction on health recommender systems.

Science.gov (United States)

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

2015-01-01

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

Adherence to Radiology Recommendations in a Clinical CT Lung Screening Program.

Science.gov (United States)

Alshora, Sama; McKee, Brady J; Regis, Shawn M; Borondy Kitts, Andrea K; Bolus, Christopher C; McKee, Andrea B; French, Robert J; Flacke, Sebastian; Wald, Christoph

2018-02-01

Assess patient adherence to radiologist recommendations in a clinical CT lung cancer screening program. Patients undergoing CT lung cancer screening between January 12, 2012, and June 12, 2013, were included in this institutional review board-approved retrospective review. Patients referred from outside our institution were excluded. All patients met National Comprehensive Cancer Network Guidelines Lung Cancer Screening high-risk criteria. Full-time program navigators used a CT lung screening program management system to schedule patient appointments, generate patient result notification letters detailing the radiologist follow-up recommendation, and track patient and referring physician notification of missed appointments at 30, 60, and 90 days. To be considered adherent, patients could be no more than 90 days past due for their next recommended examination as of September 12, 2014. Patients who died, were diagnosed with cancer, or otherwise became ineligible for screening were considered adherent. Adherence rates were assessed across multiple variables. During the study interval, 1,162 high-risk patients were screened, and 261 of 1,162 (22.5%) outside referrals were excluded. Of the remaining 901 patients, 503 (55.8%) were male, 414 (45.9%) were active smokers, 377 (41.8%) were aged 65 to 73, and >95% were white. Of the 901 patients, 772 (85.7%) were adherent. Most common reasons for nonadherence were patient refusal of follow-up exam (66.7%), inability to successfully contact the patient (20.9%), and inability to obtain the follow-up order from the referring provider (7.8%); 23 of 901 (2.6%) were discharged for other reasons. High rates of adherence to radiologist recommendations are achievable for in-network patients enrolled in a clinical CT lung screening program. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

A recommended program of tritium monitoring research and development

International Nuclear Information System (INIS)

Nickerson, S.B.; Gerdingh, R.F.; Penfold, K.

1982-10-01

This report presents recommendations for programs of research and development in tritium monitoring instrumentation. These recommendations, if implemented, will offer Canadian industry the opportunity to develop marketable instruments. The major recommendations are to assist in the development and promotion of two Chalk River Nuclear Laboratories' monitors and an Ontario Hydro monitor, and to support research and development of a surface monitor

Cryptographically-Enhanced Privacy for Recommender Systems

NARCIS (Netherlands)

Jeckmans, Arjan

2014-01-01

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

Recommended Resources for Planning to Evaluate Program Improvement Efforts (Including the SSIP)

Science.gov (United States)

National Center for Systemic Improvement at WestEd, 2015

2015-01-01

This document provides a list of recommended existing resources for state Part C and Part B 619 staff and technical assistance (TA) providers to utilize to support evaluation planning for program improvement efforts (including the State Systemic Improvement Plan, SSIP). There are many resources available related to evaluation and evaluation…

Context-aware recommender system based on ontology for recommending tourist destinations at Bandung

Science.gov (United States)

Rizaldy Hafid Arigi, L.; Abdurahman Baizal, Z. K.; Herdiani, Anisa

2018-03-01

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).

Learning Management System with Prediction Model and Course-Content Recommendation Module

Science.gov (United States)

Evale, Digna S.

2017-01-01

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…

Recommendation System for Adaptive Learning.

Science.gov (United States)

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

2018-01-01

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.

Geothermal Technologies Program Blue Ribbon Panel Recommendations

Energy Technology Data Exchange (ETDEWEB)

none,

2011-06-17

The Geothermal Technologies Program assembled a geothermal Blue Ribbon Panel on March 22-23, 2011 in Albuquerque, New Mexico for a guided discussion on the future of geothermal energy in the United States and the role of the DOE Program. The Geothermal Blue Ribbon Panel Report captures the discussions and recommendations of the experts. An addendum is available here: http://www.eere.energy.gov/geothermal/pdfs/gtp_blue_ribbon_panel_report_addendum10-2011.pdf

Monitoring program design recommendations for uranium mining communities

International Nuclear Information System (INIS)

1978-10-01

Environmental radiological monitoring requirements and their rationale have been developed for operating uranium mine/mill sites including the pre-operational phase, and for non-operating tailings areas, in order to assess the radiological impact on the environment and follow long-term trends. These recommendations have been based on a review of regulatory standards, sources and nature of releases from mines, mills and tailings, and environmental pathway analysis. Media and measurements considered in the routine on-going programs include airborne radon, airborne particulates, external radiation, terrestrial biota, surface water, drinking water, ground water, fish and sediment. Program implementation guides are provided. An overview of sampling and field technique and specific recommendations have been made. (auth)

RECOMMENDER SYSTEMS IN E-COMMERCE APPLICATIONS

Directory of Open Access Journals (Sweden)

Gyöngyvér KOVÁCS

2016-12-01

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

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

Directory of Open Access Journals (Sweden)

SOARES, M.

2014-02-01

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

Recommended system of application and development

Science.gov (United States)

Wang, Wei

2018-04-01

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.

Hybrid context aware recommender systems

Science.gov (United States)

Jain, Rajshree; Tyagi, Jaya; Singh, Sandeep Kumar; Alam, Taj

2017-10-01

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.

Online Financial Education Programs: Theory, Research, and Recommendations

Directory of Open Access Journals (Sweden)

Jinhee Kim

2017-03-01

Full Text Available Technological advances have created unprecedented opportunities for online financial education that can be used to improve financial literacy and money management practices. While online financial education programs have become popular, relevant research and theoretical frameworks have rarely been considered in the development of such programs. This article synthesizes lessons from literature and theories for the development of an effective online financial education program. Drawing from literature on financial literacy education and online education, implications and recommendations for integrating technology into online financial education programs for adults are discussed.

China Green Lights Program: A Review and Recommendations

Energy Technology Data Exchange (ETDEWEB)

Lin, Jiang

1999-06-10

This report reviews the development of China's Green Lights Program in the last two years, and discusses the remaining barriers to the widespread adoption of efficient lighting technologies in China: chiefly quality, high initial costs, and lack of accurate information. A variety of policy options are recommended for the future expansion of China's Green Lights Program.

Uncovering the information core in recommender systems

Science.gov (United States)

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

2014-08-01

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

RECOMMENDER SYSTEMS IN SOCIAL NETWORKS

Directory of Open Access Journals (Sweden)

Cleomar Valois Batista Jr

2011-12-01

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

International service learning programs: ethical issues and recommendations.

Science.gov (United States)

Reisch, Rebecca A

2011-08-01

Inequities in global health are increasingly of interest to health care providers in developed countries. In response, many academic healthcare programs have begun to offer international service learning programs. Participants in these programs are motivated by ethical principles, but this type of work presents significant ethical challenges, and no formalized ethical guidelines for these activities exist. In this paper the ethical issues presented by international service learning programs are described and recommendations are made for how academic healthcare programs can carry out international service learning programs in a way that minimizes ethical conflicts and maximizes benefits for all stakeholders. Issues related to project sustainability and community involvement are emphasized. © 2011 Blackwell Publishing Ltd.

A general framework for intelligent recommender systems

Directory of Open Access Journals (Sweden)

Jose Aguilar

2017-07-01

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.

A manual of recommended practices for hydrogen energy systems

Energy Technology Data Exchange (ETDEWEB)

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

1997-12-31

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

Receiving recommendations and providing feedback : the user-experience of a recommender system

NARCIS (Netherlands)

Knijnenburg, B.P.; Willemsen, M.C.; Hirtbach, S.; Buccafurri, F.; Semeraro, G.

2010-01-01

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 Definition of Novelty in Recommendation System

Directory of Open Access Journals (Sweden)

Liang Zhang

2013-01-01

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

TDA Assessment of Recommendations for Space Data System Standards

Science.gov (United States)

Posner, E. C.; Stevens, R.

1984-01-01

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.

Explaining the user experience of recommender systems

NARCIS (Netherlands)

Knijnenburg, B.P.; Willemsen, M.C.; Gantner, Z.; Soncu, H.; Newell, C.

2012-01-01

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

Satellite Power System (SPS) Program Summary

Energy Technology Data Exchange (ETDEWEB)

1978-12-01

The joint DOE/NASA SPS program has as its objective to achieve by the end of 1980 an initial understanding of the technical feasibility, economic practicability, and the social and environmental acceptability of the SPS concepts so that recommendations concerning program continuation can be made. The four major study areas include (1) systems definition; (2) environmental assessment; (3) societal assessment; and (4) comparative assessment of alternative energy systems. All the projects on the SPS program are listed and summarized for FY 1978. (WHK)

Avoiding congestion in recommender systems

International Nuclear Information System (INIS)

Ren, Xiaolong; Lü, Linyuan; Liu, Runran; Zhang, Jianlin

2014-01-01

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)

Review of the Commission program for standardization of nuclear power plants and recommendations to improve standardization concepts

International Nuclear Information System (INIS)

1978-02-01

This is a report of a staff study describing the need and utility of specific changes to the Commission's standardization program. The various matters considered in the study include: (1) A discussion of industry use to date of the standardization program. (2) A discussion of the experience to date with each of the standardization concepts. (3) A review of public comments on the standardization program and the staff response to each principal comment. (4) A review of the need for standardization considering the likely number of license applications to be submitted in the coming years. (5) A discussion of the reference system concept, including review of applicable experience and recommended changes to the concept. (6) A discussion of the duplicate plant concept, including review of applicable experience and recommended changes to the concept. (7) A discussion of the manufacturing license concept, including review of applicable experience and recommended changes to the concept. (8) A discussion of the replicate plant concept, including review of applicable experience and recommended changes to the concept. (9) A discussion of the effective periods for approved designs under all four standardization concepts. (10) A description of continuing staff activities related to the standardization program

Promoting cold-start items in recommender systems.

Science.gov (United States)

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

2014-01-01

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

Promoting Cold-Start Items in Recommender Systems

Science.gov (United States)

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

2014-01-01

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

Recommendation-Aware Smartphone Sensing System

OpenAIRE

Chen, Mu-Yen; Wu, Ming-Ni; Chen, Chia-Chen; Chen, Young-Long; Lin, Hsien-En

2014-01-01

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...

LDA-Based Unified Topic Modeling for Similar TV User Grouping and TV Program Recommendation.

Science.gov (United States)

Pyo, Shinjee; Kim, Eunhui; Kim, Munchurl

2015-08-01

Social TV is a social media service via TV and social networks through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned: grouping of similar TV users to create social TV communities and recommending TV programs based on group and personal interests for personalizing TV. In this paper, we propose a unified topic model based on grouping of similar TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two latent Dirichlet allocation (LDA) models. One is a topic model of TV users, and the other is a topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs, which enforces the grouping of similar TV users and associated description words for watched TV programs at the same time in a unified topic modeling framework. The unified model identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with similar tastes can be grouped as topics, which can then be recommended as social TV communities. To verify our proposed method of unified topic-modeling-based TV user grouping and TV program recommendation for social TV services, in our experiments, we used real TV viewing history data and electronic program guide data from a seven-month period collected by a TV poll agency. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation and its performance is an average of 6.5% higher than that of the topic model of TV users only. For TV user prediction with new TV programs, the average

Recent developments in affective recommender systems

Science.gov (United States)

Katarya, Rahul; Verma, Om Prakash

2016-11-01

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

China Green Lights Program: A Review and Recommendations; TOPICAL

International Nuclear Information System (INIS)

Lin, Jiang

1999-01-01

This report reviews the development of China's Green Lights Program in the last two years, and discusses the remaining barriers to the widespread adoption of efficient lighting technologies in China: chiefly quality, high initial costs, and lack of accurate information. A variety of policy options are recommended for the future expansion of China's Green Lights Program

Personalized Recommender System for Digital Libraries

Science.gov (United States)

Omisore, M. O.; Samuel, O. W.

2014-01-01

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…

An Agent Framework of Tourism Recommender System

Directory of Open Access Journals (Sweden)

Jia Zhi Yang

2016-01-01

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

Recommender Systems in Technology Enhanced Learning

NARCIS (Netherlands)

Manouselis, Nikos; Drachsler, Hendrik; Vuorikari, Riina; Hummel, Hans; Koper, Rob

2010-01-01

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.

Panorama of recommender systems to support learning

OpenAIRE

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

2015-01-01

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...

Panorama of Recommender Systems to Support Learning

NARCIS (Netherlands)

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

2015-01-01

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

Evaluation and recommendations on U.C. Lawrence Livermore Labortory Quality Assurance Program

International Nuclear Information System (INIS)

Carpenter, F.D.; Horner, M.H.

1978-01-01

A study was conducted of the University of California's Lawrence Livermore Laboratory Quality Assurance Program, which focused on training needs and recommendations tailored to the various on-going programs. Specific attention was directed to an assessment of the quality status for the MFTF facility and the capabilities of assigned quality project engineers. Conclusions and recommendations are presented which not only address the purpose of this study, but extend into other areas to provide insight and needs for a total cost effective application of a quality assurance program

weHelp: A Reference Architecture for Social Recommender Systems.

Science.gov (United States)

Sheth, Swapneel; Arora, Nipun; Murphy, Christian; Kaiser, Gail

2010-01-01

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.

Recommender Systems in Technology Enhanced Learning

NARCIS (Netherlands)

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

2010-01-01

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,

TOURISM RECOMMENDATION SYSTEM: EMPIRICAL INVESTIGATION

OpenAIRE

Biljana PETREVSKA; Saso KOCESKI

2012-01-01

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

DEVELOPMENT OF A GENERIC RECOMMENDER SYSTEM

Directory of Open Access Journals (Sweden)

Dan Munteanu

2004-12-01

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

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

Directory of Open Access Journals (Sweden)

Wataru Iwasaki

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

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

Science.gov (United States)

Iwasaki, Wataru; Yamamoto, Yasunori; Takagi, Toshihisa

2010-12-13

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

Do recommender systems benefit users? a modeling approach

Science.gov (United States)

Yeung, Chi Ho

2016-04-01

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

A trajectory-based recommender system for tourism

OpenAIRE

Baraglia, Ranieri; Frattari, Claudio; Muntean, Cristina Ioana; Nardini, Franco Maria; Silvestri, Fabrizio

2012-01-01

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...

Therapy Decision Support Based on Recommender System Methods.

Science.gov (United States)

Gräßer, Felix; Beckert, Stefanie; Küster, Denise; Schmitt, Jochen; Abraham, Susanne; Malberg, Hagen; Zaunseder, Sebastian

2017-01-01

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.

Using a Recommender System and Hyperwave Attributes To Augment an Electronic Resource Library.

Science.gov (United States)

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…

Therapy Decision Support Based on Recommender System Methods

Directory of Open Access Journals (Sweden)

Felix Gräßer

2017-01-01

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.

Recommendation System Based on Fuzzy Cognitive Map

OpenAIRE

Wei Liu; Linzhi Gao

2014-01-01

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...

Architecture and User-Context Models of CoCare: A Context-Aware Mobile Recommender System for Health Promotion.

Science.gov (United States)

Cerón-Rios, Gineth; López, Diego M; Blobel, Bernd

2017-01-01

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.

Privacy in Recommender Systems

NARCIS (Netherlands)

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

Recommendations of the workshop on advanced geothermal drilling systems

Energy Technology Data Exchange (ETDEWEB)

Glowka, D.A.

1997-12-01

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

The Evaluator's Role in Recommending Program Closure: A Model for Decision Making and Professional Responsibility

Science.gov (United States)

Eddy, Rebecca M.; Berry, Tiffany

2009-01-01

Evaluators face challenges when programs consistently fail to meet expectations for performance or improvement and consequently, evaluators may recommend that closing a program is the most prudent course of action. However, the evaluation literature provides little guidance regarding when an evaluator might recommend program closure. Given…

Archetypal Game Recommender Systems

DEFF Research Database (Denmark)

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

2014-01-01

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

Modeling mutual feedback between users and recommender systems

Science.gov (United States)

Zeng, An; Yeung, Chi Ho; Medo, Matúš; Zhang, Yi-Cheng

2015-07-01

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

A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

Directory of Open Access Journals (Sweden)

Yibo Wang

2018-01-01

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.

Weighted hybrid technique for recommender system

Science.gov (United States)

Suriati, S.; Dwiastuti, Meisyarah; Tulus, T.

2017-12-01

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.

Scientific and educational recommender systems

Science.gov (United States)

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

2017-01-01

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

Private personalized social recommendations in an IPTV system

Science.gov (United States)

Elmisery, Ahmed M.

2014-04-01

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

Collaborative Recurrent Neural Networks forDynamic Recommender Systems

Science.gov (United States)

2016-11-22

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

TOURISM RECOMMENDATION SYSTEM: EMPIRICAL INVESTIGATION

Directory of Open Access Journals (Sweden)

Biljana PETREVSKA

2012-12-01

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

Indonesian News Harvester and Recommender System

Directory of Open Access Journals (Sweden)

Adi Wibowo

2017-09-01

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.

Implementing an excellence in teaching recognition system: needs analysis and recommendations.

Science.gov (United States)

Schindler, Nancy; Corcoran, Julia C; Miller, Megan; Wang, Chih-Hsiung; Roggin, Kevin; Posner, Mitchell; Fryer, Jonathan; DaRosa, Debra A

2013-01-01

Teaching awards have been suggested to serve a variety of purposes. The specific characteristics of teaching awards and the associated effectiveness at achieving planned purposes are poorly understood. A needs analysis was performed to inform recommendations for an Excellence in Teaching Recognition System to meet the needs of surgical education leadership. We performed a 2-part needs analysis beginning with a review of the literature. We then, developed, piloted, and administered a survey instrument to General Surgery program leaders. The survey examined the features and perceived effectiveness of existing teaching awards systems. A multi-institution committee of program directors, clerkship directors, and Vice-Chairs of education then met to identify goals and develop recommendations for implementation of an "Excellence in Teaching Recognition System." There is limited evidence demonstrating effectiveness of existing teaching awards in medical education. Evidence supports the ability of such awards to demonstrate value placed on teaching, to inspire faculty to teach, and to contribute to promotion. Survey findings indicate that existing awards strive to achieve these purposes and that educational leaders believe awards have the potential to do this and more. Leaders are moderately satisfied with existing awards for providing recognition and demonstrating value placed on teaching, but they are less satisfied with awards for motivating faculty to participate in teaching or for contributing to promotion. Most departments and institutions honor only a few recipients annually. There is a paucity of literature addressing teaching recognition systems in medical education and little evidence to support the success of such systems in achieving their intended purposes. The ability of awards to affect outcomes such as participation in teaching and promotion may be limited by the small number of recipients for most existing awards. We propose goals for a Teaching Recognition

Privacy-Preserving Content-Based Recommender System

NARCIS (Netherlands)

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

2012-01-01

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

Lime and fertilizer recommendation system for coconut trees

Directory of Open Access Journals (Sweden)

Gustavo Nogueira Guedes Pereira Rosa

2011-02-01

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

Interpreting Contextual Effects By Contextual Modeling In Recommender Systems

OpenAIRE

Zheng, Yong

2017-01-01

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...

Recommendations for pneumococcal immunization outside routine childhood immunization programs in Western Europe.

Science.gov (United States)

Castiglia, Paolo

2014-10-01

The global burden of pneumococcal diseases is high, with young children and adults≥50 years of age at highest risk of infection. Two types of vaccine are available for the prevention of pneumococcal diseases caused by specific Streptococcus pneumoniae serotypes: the pneumococcal polysaccharide vaccine (PPV23) and the pneumococcal conjugate vaccine (PCV7, PCV10, and PCV13). Despite pneumococcal immunization programs in adults and children, the burden in adults has remained high. Most European countries have national or local/regional vaccination recommendations. The objective of this review was to provide an overview of the government recommendations for pneumococcal vaccination outside routine childhood vaccination programs for 16 Western European countries as of August 2014. We found that recommendations for pneumococcal immunization across Europe are complex and vary greatly among countries in terms of age groups and risk groups recommended for vaccination, as well as which vaccine should be administered. Clarifying or simplifying these recommendations and improving their dissemination could help to increase pneumococcal vaccine uptake and decrease the high burden of pneumococcal diseases in adults, both through a direct effect of the vaccine and via a herd effect in unvaccinated individuals.

An effective collaborative movie recommender system with cuckoo search

Directory of Open Access Journals (Sweden)

Rahul Katarya

2017-07-01

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 [1] and also has improvement of our previous work (0.75 MAE [2].

A fuzzy recommendation system for daily water intake

Directory of Open Access Journals (Sweden)

Bin Dai

2016-05-01

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

Towards Geosocial Recommender Systems

NARCIS (Netherlands)

de Graaff, V.; van Keulen, Maurice; de By, R.A.; de By, Rolf A.

2012-01-01

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

A Transitive Recommendation System for Information Technology Communities

Directory of Open Access Journals (Sweden)

Hesham Ali

2013-06-01

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

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Directory of Open Access Journals (Sweden)

Longbing Cao

2016-06-01

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.

Creating adaptive web recommendation system based on user behavior

Science.gov (United States)

Walek, Bogdan

2018-01-01

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.

A Group Recommender System for Tourist Activities

Science.gov (United States)

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.

Online evaluation of point-of-interest recommendation systems

NARCIS (Netherlands)

Dean-Hall, A.; Clarke, C.L.A.; Kamps, J.; Kiseleva, J.

2015-01-01

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,

Privacy enhanced recommender system

NARCIS (Netherlands)

Erkin, Zekeriya; Erkin, Zekeriya; Beye, Michael; Veugen, Thijs; Lagendijk, Reginald L.

2010-01-01

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

Useful and usable alarm systems : recommended properties

International Nuclear Information System (INIS)

Veland, Oeystein; Kaarstad, Magnhild; Seim, Lars Aage; Foerdestroemmen, Nils T.

2001-01-01

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)

TDCCREC: AN EFFICIENT AND SCALABLE WEB-BASED RECOMMENDATION SYSTEM

Directory of Open Access Journals (Sweden)

K.Latha

2010-10-01

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

Machine learning paradigms applications in recommender systems

CERN Document Server

Lampropoulos, Aristomenis S

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

Mohammad Julashokri

2011-10-01

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

A Literature Review on Recommender Systems Algorithms, Techniques and Evaluations

Directory of Open Access Journals (Sweden)

Kasra Madadipouya

2017-07-01

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.

Recommended Systems for the Incremental Automation of the Morgue of "The Daily Texan."

Science.gov (United States)

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…

Recommender Systems for the Social Web

CERN Document Server

Pazos Arias, José J; Díaz Redondo, Rebeca P

2012-01-01

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

Recommender systems for location-based social networks

CERN Document Server

Symeonidis, Panagiotis; Manolopoulos, Yannis

2014-01-01

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

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

NARCIS (Netherlands)

A. Said (Alan); A. Bellogín Kouki (Alejandro); A.P. de Vries (Arjen)

2013-01-01

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

A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

OpenAIRE

Wang, Yibo; Wang, Mingming; Xu, Wei

2018-01-01

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...

Data Mining Methods for Recommender Systems

Science.gov (United States)

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.

A Federated Recommender System for Online Learning Environments

OpenAIRE

Zhou, Lei; El Helou, Sandy; Moccozet, Laurent; Opprecht, Laurent; Benkacem, Omar; Salzmann, Christophe; Gillet, Denis

2012-01-01

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...

Deployment of Recommender Systems: Operational and Strategic Issues

Science.gov (United States)

Ghoshal, Abhijeet

2011-01-01

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

Solving the stability-accuracy-diversity dilemma of recommender systems

Science.gov (United States)

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

2017-02-01

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

Renewable generation and storage project industry and laboratory recommendations

Energy Technology Data Exchange (ETDEWEB)

Clark, N.H.; Butler, P.C.; Cameron, C.P.

1998-03-01

The US Department of Energy Office of Utility Technologies is planning a series of related projects that will seek to improve the integration of renewable energy generation with energy storage in modular systems. The Energy Storage Systems Program and the Photovoltaics Program at Sandia National Laboratories conducted meetings to solicit industry guidance and to create a set of recommendations for the proposed projects. Five possible projects were identified and a three pronged approach was recommended. The recommended approach includes preparing a storage technology handbook, analyzing data from currently fielded systems, and defining future user needs and application requirements.

Magnetic Fusion Advisory Committee report on recommended fusion program priorities and strategy

International Nuclear Information System (INIS)

1983-09-01

The Magnetic Fusion Advisory Committee recommends a new program strategy with the following principal features: (1) Initiation in FY86 of the Tokamak Fusion Core Experiment (TFCX), a moderate-cost tokamak reactor device (less than $1 B PACE) designed to achieve ignition and long-pulse equilibrium burn. Careful trade-off studies are needed before making key design choices in interrelated technology areas. Cost reductions relative to earlier plans can be realized by exploiting new plasma technology, by locating the TFCX at the TFTR site, and by assigning responsibility for complementary reactor engineering tasks to other sectors of the fusion program. (2) Potential utilization of the MFTF Upgrade to provide a cost-effective means for quasi-steady-state testing of blanket and power-system components, complementary to TFCX. This will depend on future assessments of the data base for tandem mirrors. (3) Vigorous pursuit of the broad US base program in magnetic confinement, including new machine starts, where appropriate, at approximately the present total level of support. (4) Utilization of Development and Technology programs in plasma and magnet technology in support of specific hardware requirements of the TFCX and of other major fusion facilities, so as to minimize overall program cost

Compensation programs after withdrawal of the recommendation for HPV vaccine in Japan.

Science.gov (United States)

Yuji, Koichiro; Nakada, Haruka

2016-05-03

HPV vaccinations were recommended with the backing of a Japanese government subsidy program in 2010, and were included in the National Immunization Program in April 2013. However, the Ministry of Health, Labour, and Welfare withdrew the recommendation for the HPV vaccination in June 2013. We investigated HPV vaccine injury compensation programs for both the national and local governments. Approximately 3.38 million girls were vaccinated, and 2,584 complained of health problems. The majority of these received the vaccine shot as a non-routine vaccination. In total, 98 people developed health problems and applied for assistance from 2011 to 2014, but no cases have been processed since October 2014. Several local governments are providing their own compensation program for cases of vaccine adverse reactions, but the number is extremely low (16 of 1,741 municipalities and 1 of 47 prefectures). The local governments that are providing compensation are largely those where HPV vaccine victim support groups are prominent. The confusion regarding the national program for HPV vaccine injury was caused by the discrepancy between the compensation programs for those vaccinated under the immunization law and for those who received voluntary vaccinations. The establishment of a new compensation program might be key to finding a lasting resolution.

Research and Design of a Grid Based Electronic Commerce Recommendation System

OpenAIRE

Liang, Yueling; Nie, Guihua

2010-01-01

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...

Marketing Recommender Systems: A New Approach in Digital Economy

Directory of Open Access Journals (Sweden)

Loredana MOCEAN

2012-01-01

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

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

Directory of Open Access Journals (Sweden)

Farman Ullah

2014-01-01

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.

A network and visual quality aware N-screen content recommender system using joint matrix factorization.

Science.gov (United States)

Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang

2014-01-01

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.

Recommendation Report: EJournals/EBooks A-Z Management System

KAUST Repository

Ramli, Rindra M.

2014-01-01

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

CitRec 2017 : International Workshop on Recommender Systems for Citizens

NARCIS (Netherlands)

Yang, J.; Sun, Zhu; Bozzon, A.; Zhang, J.; Larson, M.A.

2017-01-01

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.

DRARS, A Dynamic Risk-Aware Recommender System

OpenAIRE

Bouneffouf , Djallel

2013-01-01

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...

Assessment of the NASA Space Shuttle Program's Problem Reporting and Corrective Action System

Science.gov (United States)

Korsmeryer, D. J.; Schreiner, J. A.; Norvig, Peter (Technical Monitor)

2001-01-01

This paper documents the general findings and recommendations of the Design for Safety Programs Study of the Space Shuttle Programs (SSP) Problem Reporting and Corrective Action (PRACA) System. The goals of this Study were: to evaluate and quantify the technical aspects of the SSP's PRACA systems, and to recommend enhancements addressing specific deficiencies in preparation for future system upgrades. The Study determined that the extant SSP PRACA systems accomplished a project level support capability through the use of a large pool of domain experts and a variety of distributed formal and informal database systems. This operational model is vulnerable to staff turnover and loss of the vast corporate knowledge that is not currently being captured by the PRACA system. A need for a Program-level PRACA system providing improved insight, unification, knowledge capture, and collaborative tools was defined in this study.

Hybrid attacks on model-based social recommender systems

Science.gov (United States)

Yu, Junliang; Gao, Min; Rong, Wenge; Li, Wentao; Xiong, Qingyu; Wen, Junhao

2017-10-01

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.

Mobile Application Recommender System

OpenAIRE

Davidsson, Christoffer

2010-01-01

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...

Family Shopping Recommendation System Using User Profile and Behavior Data

OpenAIRE

Jiacheng, Xu

2017-01-01

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...

Goal-based structuring in a recommender systems

NARCIS (Netherlands)

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

Recommendations on future development of decision support systems

DEFF Research Database (Denmark)

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...

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

Directory of Open Access Journals (Sweden)

Jesus G. Boticario

2011-07-01

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

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Science.gov (United States)

Erdt, Mojisola; Fernandez, Alejandro; Rensing, Christoph

2015-01-01

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…

Recommended research program for improving seismic safety of light-water nuclear power plants

International Nuclear Information System (INIS)

1979-04-01

Recommendations are presented for research areas concerned with seismic safety. These recommendations are based on an analysis of the answers to a questionnaire which was sent to over 80 persons working in the area of seismic safety of nuclear power plants. In addition to the answers of the 55 questionnaires which were received, the recommendations are based on ideas expressed at a meeting of an ad hoc group of professionals formed by Sandia, review of literature, current research programs, and engineering judgement

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

Science.gov (United States)

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

2014-01-01

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

Recommendations for strengthening the infrared technology component of any condition monitoring program

Science.gov (United States)

Nicholas, Jack R., Jr.; Young, R. K.

1999-03-01

This presentation provides insights of a long term 'champion' of many condition monitoring technologies and a Level III infra red thermographer. The co-authors present recommendations based on their observations of infra red and other components of predictive, condition monitoring programs in manufacturing, utility and government defense and energy activities. As predictive maintenance service providers, trainers, informal observers and formal auditors of such programs, the co-authors provide a unique perspective that can be useful to practitioners, managers and customers of advanced programs. Each has over 30 years experience in the field of machinery operation, maintenance, and support the origins of which can be traced to and through the demanding requirements of the U.S. Navy nuclear submarine forces. They have over 10 years each of experience with programs in many different countries on 3 continents. Recommendations are provided on the following: (1) Leadership and Management Support (For survival); (2) Life Cycle View (For establishment of a firm and stable foundation for a program); (3) Training and Orientation (For thermographers as well as operators, managers and others); (4) Analyst Flexibility (To innovate, explore and develop their understanding of machinery condition); (5) Reports and Program Justification (For program visibility and continued expansion); (6) Commitment to Continuous Improvement of Capability and Productivity (Through application of updated hardware and software); (7) Mutual Support by Analysts (By those inside and outside of the immediate organization); (8) Use of Multiple Technologies and System Experts to Help Define Problems (Through the use of correlation analysis of data from up to 15 technologies. An example correlation analysis table for AC and DC motors is provided.); (9) Root Cause Analysis (Allows a shift from reactive to proactive stance for a program); (10) Master Equipment Identification and Technology Application (To

Social network supported process recommender system.

Science.gov (United States)

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

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.

Social Network Supported Process Recommender System

Directory of Open Access Journals (Sweden)

Yanming Ye

2014-01-01

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.

Building Personalized and Non Personalized Recommendation Systems

OpenAIRE

SNEHA KHATWANI; DR. M.B. CHANDAK

2016-01-01

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...

Measuring Learner's Performance in E-Learning Recommender Systems

Science.gov (United States)

Ghauth, Khairil Imran; Abdullah, Nor Aniza

2010-01-01

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).…

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

Science.gov (United States)

Mihai, Gabroveanu

2015-09-01

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

Evaluation of nuclear power plant environmental impact prediction, based on monitoring programs. Summary and recommendations

International Nuclear Information System (INIS)

Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

1977-02-01

An evaluation of the effectivenss of non-radiological environmental monitoring programs is presented. The monitoring programs for Monticello, Haddam Neck, and Millstone Nuclear Generating Plants are discussed. Recommendations for improvements in monitoring programs are presented

A Recommendation System to Facilitate Business Process Modeling.

Science.gov (United States)

Deng, Shuiguang; Wang, Dongjing; Li, Ying; Cao, Bin; Yin, Jianwei; Wu, Zhaohui; Zhou, Mengchu

2017-06-01

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.

Distributed Online Learning in Social Recommender Systems

Science.gov (United States)

Tekin, Cem; Zhang, Simpson; van der Schaar, Mihaela

2014-08-01

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.

Assessment of current NRC/IE professional training program and recommendations for improvement

International Nuclear Information System (INIS)

Bartley, H.J.; Hagerup, J.E.; Harrison, O.J.; Heyer, F.H.K.; Kaas, I.W.; Schwartz, E.G.

1978-05-01

This document is the General Research Corporation (GRC) report on Task III: to assess the current NRC/IE professional training program and to provide recommendations for improvement. The major objectives of this task were to determine the overall effectiveness of the NRC/IE training program and to provide recommendations for improvements where appropriate. The research involved a review of course manuals and of student critiques, observation in the classroom and person to person interviews; it also included an evaluation of the assignment of instructors to the Career Management Branch. Findings addressed refresher training, retread training and initial training--with emphasis on the last of these. Conclusions are that: (1) The curriculum provides, in general, types and levels of training needed; (2) the mix of training methods used is correct; and (3) the training management is effective. However, the training facilities do not reflect a commitment to quality instruction nor is assignment as instructor to the Career Management Branch attractive to inspectors. Recommendations presented in the report are based upon the findings; all lie within the implementing authority of Headquarters NRC/IE

Models and methods for building web recommendation systems

OpenAIRE

Stekh, Yu.; Artsibasov, V.

2012-01-01

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...

A collaborative approach for research paper recommender system.

Science.gov (United States)

Haruna, Khalid; Akmar Ismail, Maizatul; Damiasih, Damiasih; Sutopo, Joko; Herawan, Tutut

2017-01-01

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.

Commentary: Recommendations and remaining questions for health care leadership training programs.

Science.gov (United States)

Stoller, James K

2013-01-01

Effective leadership is critical for optimizing cost, access, and quality in health care. Creating a pipeline of effective health care leaders requires developing leadership competencies that differ from the usual criteria of clinical and scientific excellence by which physicians have traditionally been promoted to leadership positions. Specific competencies that differentiate effective leaders from average leaders, especially emotional intelligence and its component abilities, are essential for effective leadership.Adopting a long-standing practice from successful corporations, some health care institutions, medical societies, and business schools now offer leadership programs that address these differentiating leadership competencies. The author draws on experience with such programs through the Cleveland Clinic Academy to provide recommendations for health care leadership training and to identify unanswered questions about such programs.The author recommends that such training should be broadly available to all health care leadership communities (i.e., nurses, administrators, and physicians). A progressive curriculum, starting with foundational concepts and extending to coaching and feedback opportunities through experiential learning, recognizes the challenge of becoming an effective leader and the long time line needed to do so. Linking leadership courses to continuing medical education and to graduate credit opportunities is appealing to participants. Other recommendations focus on the importance of current leaders' involvement in nominating emerging leaders for participation, embedding leadership development discussions in faculty's professional reviews, and blending discussion of frameworks and theory with practical, experiential lessons. The author identifies questions about the benefits of formal health care leadership training that remain to be answered.

Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword

OpenAIRE

Suka Parwita, Wayan Gede; Winarko, Edi

2015-01-01

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...

Understanding Cooperative Learning in Context-aware Recommender Systems

DEFF Research Database (Denmark)

Jiang, Na; Tan, Chee-Wee; Wang, Weiquan

2017-01-01

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....

An Ontology-Based Tourism Recommender System Based on Spreading Activation Model

Science.gov (United States)

Bahramian, Z.; Abbaspour, R. Ali

2015-12-01

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.

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

Directory of Open Access Journals (Sweden)

Z. Bahramian

2015-12-01

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

Recommended regulatory program plan for low-level radioactive waste management in Maryland

International Nuclear Information System (INIS)

1986-01-01

The National Program for Low-Level Radioactive Waste Management was instituted by the US Department of Energy to assist the states in carrying out this new federal policy. Based on the premise that the safe disposal of low-level waste is technologically feasible and that states have the necessary degree of authority to set management policy, the National Program is helping them to develop a responsive, comprehensive regulatory program. The State of Maryland is actively engaged with the National Program in its efforts to form a comprehensive management program. The purpose of this plan is to review existing statutory and regulatory program responsibilities and provide a recommended management scheme for low-level radioactive waste

Teaching and Assessing Systems-based Competency in Ophthalmology Residency Training Programs

NARCIS (Netherlands)

Lee, Andrew G.; Beaver, Hilary A.; Greenlee, Emily; Oetting, Thomas A.; Boldt, H. Culver; Olson, Richard; Abramoff, Michael; Carter, Keith

2007-01-01

The Accreditation Council for Graduate Medical Education (ACGME) has mandated that residency programs, including ophthalmology, teach and assess specific competencies, including systems-based learning. We review the pertinent literature on systems-based learning for ophthalmology and recommend

TrustRank: a Cold-Start tolerant recommender system

Science.gov (United States)

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

2015-02-01

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

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

OpenAIRE

Jesus G. Boticario; Olga C. Santos

2011-01-01

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 ...

Incorporating popularity in a personalized news recommender system

Directory of Open Access Journals (Sweden)

Nirmal Jonnalagedda

2016-06-01

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.

Personalised Information Gathering and Recommender Systems: Techniques and Trends

Directory of Open Access Journals (Sweden)

Xiaohui Tao

2013-02-01

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

Proactive Recommender Systems in Automotive Scenarios

OpenAIRE

Bader, Roland

2013-01-01

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...

Integrating Information Extraction Agents into a Tourism Recommender System

Science.gov (United States)

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

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

Pilot trial of IOM duty hour recommendations in neurology residency programs: unintended consequences.

Science.gov (United States)

Schuh, L A; Khan, M A; Harle, H; Southerland, A M; Hicks, W J; Falchook, A; Schultz, L; Finney, G R

2011-08-30

To study the potential effect of the 2008 Institute of Medicine (IOM) work duty hour (WDH) recommendations on neurology residency programs. This study evaluated resident sleepiness, personal study hours, quality of life, and satisfaction and faculty satisfaction during a control month using the Accreditation Council for Graduate Medical Education WDH requirements and during an intervention month using the IOM WDH recommendations. Resident participation in both schedules was mandatory, but both resident and faculty participation in the outcome measures was voluntary. Thirty-four residents (11 postgraduate year [PGY]-4, 9 PGY-3, and 14 PGY-2) participated. End-of-work shift sleepiness, mean weekly sleep hours, personal study hours, and hours spent in lectures did not differ between the control and intervention months. Resident quality of life measured by the Maslach Burnout Inventory declined for 1 subscore in the intervention month (p = 0.03). Resident education satisfaction declined during the intervention month for issues related to continuity of care, patient hand-offs, and knowledge of their patients. Faculty satisfaction declined during the intervention month, without a decline in quality of life. The results from 3 residency programs suggest that the IOM WDH recommendations may negatively affect neurology resident education. This study was limited by the short duration of implementation, negative bias against the IOM recommendations, and inability to blind faculty. Additional study of the IOM WDH recommendations is warranted before widespread implementation.

E-Learning Recommender System Based on Collaborative Filtering and Ontology

OpenAIRE

John Tarus; Zhendong Niu; Bakhti Khadidja

2017-01-01

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 ...

Which recommender system can best fit social learning platforms?

NARCIS (Netherlands)

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

2014-01-01

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

Which Recommender System Can Best Fit Social Learning Platforms?

NARCIS (Netherlands)

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

2014-01-01

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

Towards a Legal Recommender System

NARCIS (Netherlands)

Winkels, R.; Boer, A.; Vredebregt, B.; van Someren, A.

2014-01-01

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,

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

NARCIS (Netherlands)

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

2007-01-01

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

Collaborative User Network Embedding for Social Recommender Systems

KAUST Repository

Zhang, Chuxu; Yu, Lu; Wang, Yan; Shah, Chirag; Zhang, Xiangliang

2017-01-01

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

Algorithms for Academic Search and Recommendation Systems

DEFF Research Database (Denmark)

Amolochitis, Emmanouil

2014-01-01

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...

Recommender systems for technology enhanced learning research trends and applications

CERN Document Server

Manouselis, Nikos; Verbert, Katrien

2014-01-01

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

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

Science.gov (United States)

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

2011-05-01

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

Shilling Attack Prevention for Recommender Systems Using Social-based Clustering

KAUST Repository

Lee, Tak

2011-06-06

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

FERTILIZER RECOMMENDATION SYSTEM FOR MELON BASED ON NUTRITIONAL BALANCE

Directory of Open Access Journals (Sweden)

José Aridiano Lima de Deus

2015-04-01

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

Evaluation of Explanation Interfaces in Recommender Systems

Directory of Open Access Journals (Sweden)

Sergio Cleger-Tamayo

2017-05-01

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.

75 FR 36120 - Proposed Information Collection Request Submitted for Public Comment and Recommendations; Program...

Science.gov (United States)

2010-06-24

... Submitted for Public Comment and Recommendations; Program To Prevent Smoking Underground and in Hazardous... mine operators are required to develop programs to prevent persons from carrying smoking materials, matches, or lighters underground and to prevent smoking in hazardous areas, such as in or around oil...

Where to publish? Development of a recommender system for academic publishing

OpenAIRE

Gutknecht, Christian

2014-01-01

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...

Modification of reference temperature program in reactor regulating system

Energy Technology Data Exchange (ETDEWEB)

Yu, Sung Sik; Lee, Byung Jin; Kim, Se Chang; Cheong, Jong Sik [Korea Power Engineering Company, Inc., Seoul (Korea, Republic of); Kim, Ji In; Doo, Jin Yong [Korea Electric Power Cooperation, Yonggwang (Korea, Republic of)

1999-12-31

In Yonggwang nuclear units 3 and 4 currently under commercial operation, the cold temperature was very close to the technical specification limit of 298 deg C during initial startup testing, which was caused by the higher-than-expected reactor coolant system flow. Accordingly, the reference temperature (Tref) program needed to be revised to allow more flexibility for plant operations. In this study, the method of a specific test performed at Yonggwang nuclear unit 4 to revise the Tref program was described and the test results were discussed. In addition, the modified Tref program was evaluated on its potential impacts on system performance and safety. The methods of changing the Tref program and the associated pressurizer level setpoint program were also explained. Finally, for Ulchin nuclear unit 3 and 4 currently under initial startup testing, the effects of reactor coolant system flow rate on the coolant temperature were evaluated from the thermal hydraulic standpoint and an optimum Tref program was recommended. 6 refs., 4 figs., 2 tabs. (Author)

Modification of reference temperature program in reactor regulating system

Energy Technology Data Exchange (ETDEWEB)

Yu, Sung Sik; Lee, Byung Jin; Kim, Se Chang; Cheong, Jong Sik [Korea Power Engineering Company, Inc., Seoul (Korea, Republic of); Kim, Ji In; Doo, Jin Yong [Korea Electric Power Cooperation, Yonggwang (Korea, Republic of)

1998-12-31

In Yonggwang nuclear units 3 and 4 currently under commercial operation, the cold temperature was very close to the technical specification limit of 298 deg C during initial startup testing, which was caused by the higher-than-expected reactor coolant system flow. Accordingly, the reference temperature (Tref) program needed to be revised to allow more flexibility for plant operations. In this study, the method of a specific test performed at Yonggwang nuclear unit 4 to revise the Tref program was described and the test results were discussed. In addition, the modified Tref program was evaluated on its potential impacts on system performance and safety. The methods of changing the Tref program and the associated pressurizer level setpoint program were also explained. Finally, for Ulchin nuclear unit 3 and 4 currently under initial startup testing, the effects of reactor coolant system flow rate on the coolant temperature were evaluated from the thermal hydraulic standpoint and an optimum Tref program was recommended. 6 refs., 4 figs., 2 tabs. (Author)

Electronic Resources Management System: Recommendation Report 2017

KAUST Repository

Ramli, Rindra M.

2017-01-01

This recommendation report provides an overview of the selection process for the new Electronic Resources Management System. The library has decided to move away from Innovative Interfaces Millennium ERM module. The library reviewed 3 system

A Multi-context BDI Recommender System: from Theory to Simulation

OpenAIRE

Ben Othmane , Amel; Tettamanzi , Andrea G. B.; Villata , Serena; Le Thanh , Nhan

2016-01-01

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 ...

PERSON-Personalized Expert Recommendation System for Optimized Nutrition.

Science.gov (United States)

Chen, Chih-Han; Karvela, Maria; Sohbati, Mohammadreza; Shinawatra, Thaksin; Toumazou, Christofer

2018-02-01

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.

Personalized professional content recommendation

Science.gov (United States)

Xu, Songhua

2015-10-27

A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.

RECOMMENDED FOUNDATION FILL MATERIALS CONSTRUCTION STANDARD OF THE FLORIDA RADON RESEARCH PROGRAM

Science.gov (United States)

The report summarizes the technical basis for a recommended foundation fill materials standard for new construction houses in Florida. he radon-control construction standard was developed by the Florida Radon Research Program (FRRP). ill material standards are formulated for: (1)...

Recommendation of a More Effective Alternative to the NASA Launch Services Program Mission Integration Reporting System (MIRS) and Implementation of Updates to the Mission Plan

Science.gov (United States)

Dunn, Michael R.

2014-01-01

Over the course of my internship in the Flight Projects Office of NASA's Launch Services Program (LSP), I worked on two major projects, both of which dealt with updating current systems to make them more accurate and to allow them to operate more efficiently. The first project dealt with the Mission Integration Reporting System (MIRS), a web-accessible database application used to manage and provide mission status reporting for the LSP portfolio of awarded missions. MIRS had not gone through any major updates since its implementation in 2005, and it was my job to formulate a recommendation for the improvement of the system. The second project I worked on dealt with the Mission Plan, a document that contains an overview of the general life cycle that is followed by every LSP mission. My job on this project was to update the information currently in the mission plan and to add certain features in order to increase the accuracy and thoroughness of the document. The outcomes of these projects have implications in the orderly and efficient operation of the Flight Projects Office, and the process of Mission Management in the Launch Services Program as a whole.

Improvement of recommender systems considering big data of users ...

African Journals Online (AJOL)

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 ...

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

Science.gov (United States)

Tang, Tiffany Ya; Winoto, Pinata

2016-01-01

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

Hospital information system: reusability, designing, modelling, recommendations for implementing.

Science.gov (United States)

Huet, B

1998-01-01

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.

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

Directory of Open Access Journals (Sweden)

T N Chiranjeevi

2016-04-01

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

A Knowledge Based Recommender System with Multigranular Linguistic Information

Directory of Open Access Journals (Sweden)

Luis Martinez

2008-08-01

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.

Changing Paradigms: From Schooling to Schools as Adaptive Recommendation Systems

DEFF Research Database (Denmark)

Christiansen, René Boyer; Gynther, Karsten; Petersen, Anne Kristine

2017-01-01

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...

Changing Paradigms: From Schooling to Schools as Adaptive Recommendation Systems

DEFF Research Database (Denmark)

Christiansen, René Boyer; Gynther, Karsten; Petersen, Anne Kristine

2017-01-01

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...

Freshmen Program Withdrawal: Types and Recommendations

Directory of Open Access Journals (Sweden)

Ana Bernardo

2017-09-01

Full Text Available University program dropout is a problem that has important consequences not only for the student that leaves but also for the institution in which the withdrawal occurs. Therefore, higher education institutions must study the problem in greater depth to establish appropriate prevention measures in the future. However, most research papers currently focus primarily on the characteristics of students who leave university, rather than on those who choose to pursue alternative courses of study and therefore fail to take into account the different kinds of abandonment. The aim of this paper is to identify the different types of dropout to define their characteristics and propose some recommendations. Thus, an ex post facto study was carried out on a sample of 1,311 freshmen from a university in the north of Spain using data gathered using an ad-hoc designed questionnaire, applied by telephone or an online survey, and completed with data available in the university data warehouse. A descriptive analysis was performed to characterize the sample and identify five different groups, including 1. Students persisting in their initiated degree 2. Students who change of program (within the same university 3. Students transferring to a different university 4. Students enrolling in non-higher-education studies 5. Students that quit studying. Also, data mining techniques (decision trees were applied to classify the cases and generate predictive models to aid in the design of differentiated intervention strategies for each of the corresponding groups.

Freshmen Program Withdrawal: Types and Recommendations

Science.gov (United States)

Bernardo, Ana; Cervero, Antonio; Esteban, María; Tuero, Ellian; Casanova, Joana R.; Almeida, Leandro S.

2017-01-01

University program dropout is a problem that has important consequences not only for the student that leaves but also for the institution in which the withdrawal occurs. Therefore, higher education institutions must study the problem in greater depth to establish appropriate prevention measures in the future. However, most research papers currently focus primarily on the characteristics of students who leave university, rather than on those who choose to pursue alternative courses of study and therefore fail to take into account the different kinds of abandonment. The aim of this paper is to identify the different types of dropout to define their characteristics and propose some recommendations. Thus, an ex post facto study was carried out on a sample of 1,311 freshmen from a university in the north of Spain using data gathered using an ad-hoc designed questionnaire, applied by telephone or an online survey, and completed with data available in the university data warehouse. A descriptive analysis was performed to characterize the sample and identify five different groups, including 1. Students persisting in their initiated degree 2. Students who change of program (within the same university) 3. Students transferring to a different university 4. Students enrolling in non-higher-education studies 5. Students that quit studying. Also, data mining techniques (decision trees) were applied to classify the cases and generate predictive models to aid in the design of differentiated intervention strategies for each of the corresponding groups. PMID:28983263

Intelligent Online Store: User Behavior Analysis based Recommender System

Directory of Open Access Journals (Sweden)

Mohamadreza Karimi Alavije

2015-06-01

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.

A recommender system for prostate cancer websites.

Science.gov (United States)

Witteman, Holly; Chignell, Mark; Krahn, Murray

2008-11-06

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.

A Stock Trading Recommender System Based on Temporal Association Rule Mining

Directory of Open Access Journals (Sweden)

Binoy B. Nair

2015-04-01

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.

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

Science.gov (United States)

2013-01-01

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

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

Science.gov (United States)

Wendel, Sonja; Dellaert, Benedict G C; Ronteltap, Amber; van Trijp, Hans C M

2013-04-04

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

Location-aware News Recommendation System with Using Fuzzy Logic

Directory of Open Access Journals (Sweden)

Mehdi Nejati

2016-10-01

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

Establishing Recommendations for Stroke Systems in the Thrombectomy Era: The Upstate New York Stakeholder Proceedings.

Science.gov (United States)

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

2017-07-01

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

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

Directory of Open Access Journals (Sweden)

Felipe de Campos Carmona

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

Information filtering in sparse online systems: recommendation via semi-local diffusion.

Science.gov (United States)

Zeng, Wei; Zeng, An; Shang, Ming-Sheng; Zhang, Yi-Cheng

2013-01-01

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.

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

Directory of Open Access Journals (Sweden)

Huirui Han

2018-06-01

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.

Savannah River Site (SRS) implementation program plan for DNFSB Recommendation 90-2

International Nuclear Information System (INIS)

Talukdar, B.K.; Loceff, F.

1993-01-01

The Defense Nuclear Facilities Safety Board (DNFSB) based on its review and evaluation of the content and implementation of standards relating to design, construction, operation, and decommissioning of Defense Nuclear Facilities has made the recommendations (90-2) which when implemented would assure comparable or equivalent levels of safety to the environment, public and workers as required for the commercial nuclear facilities. DOE has accepted the DNFSB 90-2 recommendations and have directed SRS and other M ampersand Os to implement them. This report discusses implementation program which commits to developing Requirement Identification Documents (RID's) for all defense nuclear facilities in the DOE complex

Exploring implementation of the 2010 Institute of Medicine’s Child and Adult Food Care Program recommendations for after-school snacks

Science.gov (United States)

Nanney, Marilyn S; Glatt, Carissa

2012-01-01

Objective The aim of the present study was to explore the implementation of nutrition recommendations made in the 2010 Institute of Medicine (IOM) report, Child and Adult Care Food Program: Aligning Dietary Guidance for All, in school-based after-school snack programmes. Design A descriptive study. Setting One large suburban school district in Minneapolis, Minnesota, USA. Subjects None. Results Major challenges to implementation included limited access to product labelling and specifications inconsistent with the IOM’s Child and Adult Care Food Program (CACFP) recommendations, limited access to healthier foods due to current school district buying consortium agreement, and increased costs of wholegrain and lower-sodium foods and pre-packaged fruits and vegetables. Conclusions Opportunities for government and industry policy development and partnerships to support schools in their efforts to promote healthy after-school food environments remain. Several federal, state and industry leadership opportunities are proposed: provide product labelling that makes identifying snacks which comply with the 2010 IOM CACFP recommended standards easy; encourage compliance with recommendations by providing incentives to programmes; prioritize the implementation of paperwork and technology that simplifies enrolment and accountability systems; and provide support for food safety training and/or certification for non-food service personnel. PMID:22050891

TRSDL: Tag-Aware Recommender System Based on Deep Learning–Intelligent Computing Systems

Directory of Open Access Journals (Sweden)

Nan Liang

2018-05-01

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.

Extending a Hybrid Tag-Based Recommender System with Personalization

DEFF Research Database (Denmark)

Durao, Frederico; Dolog, Peter

2010-01-01

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

A LITERATURE SURVEY ON RECOMMENDATION SYSTEM BASED ON SENTIMENTAL ANALYSIS

OpenAIRE

Achin Jain; Vanita Jain; Nidhi Kapoor

2016-01-01

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...

How Recommender Systems in Technology-Enhanced Learning depend on Context

NARCIS (Netherlands)

Drachsler, Hendrik; Manouselis, Nikos

2009-01-01

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

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

Directory of Open Access Journals (Sweden)

Mohamed FRIKHA

2015-12-01

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

Personalized links recommendation based on data mining in adaptive educational hypermedia systems

NARCIS (Netherlands)

Romero, C.; Ventura, S.; Delgado, J.A.; De Bra, P.M.E.; Duval, E.; Klamma, R.; Wolpers, M.

2007-01-01

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

Recommendations to alarm systems and lessons learned on alarm system implementation

International Nuclear Information System (INIS)

Soerenssen, Aimar; Veland, Oeystein; Farbrot, Jan Erik; Kaarstad, Magnhild; Seim, Lars Aage; Foerdestroemmen, Nils; Bye, Andreas

2001-11-01

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

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Directory of Open Access Journals (Sweden)

Yan Guo

2017-10-01

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.

Social and Behavioral Aspects of a Tag-Based Recommender System

DEFF Research Database (Denmark)

Durao, Frederico; Dolog, Peter

2009-01-01

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

A Personalized Tag-Based Recommendation in Social Web Systems

DEFF Research Database (Denmark)

Durao, Frederico; Dolog, Peter

2009-01-01

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

Biosignal controlled recommendation in entertainment systems

NARCIS (Netherlands)

Liu, H.

2010-01-01

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

Privacy-preserving recommender systems in dynamic environments

NARCIS (Netherlands)

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

2013-01-01

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

Research on the Application of Persona in Book Recommendation System

Science.gov (United States)

Gao, Baozhong; Du, Shouyan; Li, Xinzhi; Liu, Fangai

2017-10-01

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.

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

Science.gov (United States)

Wiesner, Martin; Pfeifer, Daniel

2014-03-03

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

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

Directory of Open Access Journals (Sweden)

Martin Wiesner

2014-03-01

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

Coarse cluster enhancing collaborative recommendation for social network systems

Science.gov (United States)

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

2017-10-01

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

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

Directory of Open Access Journals (Sweden)

A. A. Salama

2015-03-01

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

Electronic Resources Management System: Recommendation Report 2017

KAUST Repository

Ramli, Rindra M.

2017-05-01

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

PubMedReco: A Real-Time Recommender System for PubMed Citations.

Science.gov (United States)

Samuel, Hamman W; Zaïane, Osmar R

2017-01-01

We present a recommender system, PubMedReco, for real-time suggestions of medical articles from PubMed, a database of over 23 million medical citations. PubMedReco can recommend medical article citations while users are conversing in a synchronous communication environment such as a chat room. Normally, users would have to leave their chat interface to open a new web browser window, and formulate an appropriate search query to retrieve relevant results. PubMedReco automatically generates the search query and shows relevant citations within the same integrated user interface. PubMedReco analyzes relevant keywords associated with the conversation and uses them to search for relevant citations using the PubMed E-utilities programming interface. Our contributions include improvements to the user experience for searching PubMed from within health forums and chat rooms, and a machine learning model for identifying relevant keywords. We demonstrate the feasibility of PubMedReco using BMJ's Doc2Doc forum discussions.

A Proposed Business Intelligent Framework for Recommender Systems

Directory of Open Access Journals (Sweden)

Sitalakshmi Venkatraman

2017-11-01

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.

The Design and Implementation of an Intelligent Apparel Recommend Expert System

Directory of Open Access Journals (Sweden)

A. H. Dong

2013-01-01

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

Which Recommender System Can Best Fit Social Learning Platforms?

OpenAIRE

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

2014-01-01

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

A Mobile Ecotourism Recommendations System Using Cars-Context Aware Approaches

Directory of Open Access Journals (Sweden)

Yani Nurhadryani

2013-11-01

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

Content-based Music Search and Recommendation System

Science.gov (United States)

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.

Machine learning for recommendation systems in job postings selection

OpenAIRE

Marcos Santamarta, Victor

2016-01-01

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...

Contexts in a Paper Recommendation System with Collaborative Filtering

Science.gov (United States)

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

2012-01-01

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

Group Recommendation Systems Based on External Social-Trust Networks

Directory of Open Access Journals (Sweden)

Guang Fang

2018-01-01

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.

A study of concept-based similarity approaches for recommending program examples

Science.gov (United States)

Hosseini, Roya; Brusilovsky, Peter

2017-07-01

This paper investigates a range of concept-based example recommendation approaches that we developed to provide example-based problem-solving support in the domain of programming. The goal of these approaches is to offer students a set of most relevant remedial examples when they have trouble solving a code comprehension problem where students examine a program code to determine its output or the final value of a variable. In this paper, we use the ideas of semantic-level similarity-based linking developed in the area of intelligent hypertext to generate examples for the given problem. To determine the best-performing approach, we explored two groups of similarity approaches for selecting examples: non-structural approaches focusing on examples that are similar to the problem in terms of concept coverage and structural approaches focusing on examples that are similar to the problem by the structure of the content. We also explored the value of personalized example recommendation based on student's knowledge levels and learning goal of the exercise. The paper presents concept-based similarity approaches that we developed, explains the data collection studies and reports the result of comparative analysis. The results of our analysis showed better ranking performance of the personalized structural variant of cosine similarity approach.

The pre-operational monitoring - how useful are recommendations of international organizations and various national programs

International Nuclear Information System (INIS)

Mihailovic, M.

1980-01-01

National legislation and the preoperational monitoring program around Nuclear Power Plant Krsko are described. The usefulness of international recommendations and various national preoperational monitoring programs is examined. Modifications are described which were introduced with the aim of identifying the site specific critical exposure pathways. The role of qualified and experienced experts is discussed. (H.K.)

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

OpenAIRE

T N Chiranjeevi; R H Vishwanath

2016-01-01

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...

Using Recommendation System for E-learning Environments at degree level

Directory of Open Access Journals (Sweden)

Rubén González Crespo

2009-12-01

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

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

Directory of Open Access Journals (Sweden)

Wei Jiang

2016-01-01

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

Finding Your Literature Match - A Physics Literature Recommender System

Science.gov (United States)

Henneken, Edwin; Kurtz, Michael

2010-03-01

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.

Teen Pregnancy Prevention Program Recommendations from Urban and Reservation Northern Plains American Indian Community Members

OpenAIRE

McMahon, Tracey R.; Hanson, Jessica D.; Griese, Emily R.; Kenyon, DenYelle Baete

2015-01-01

Despite declines over the past few decades, the United States has one of the highest rates of teen pregnancy compared to other industrialized nations. American Indian youth have experienced higher rates of teen pregnancy compared to the overall population for decades. Although it's known that community and cultural adaptation enhance program effectiveness, few teen pregnancy prevention programs have published on recommendations for adapting these programs to address the specific needs of Nort...

Hierarchically Structured Recommender System for Improving NPS

Science.gov (United States)

Kuang, Jieyan

2016-01-01

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…

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

Directory of Open Access Journals (Sweden)

Basma AlBanna

2016-12-01

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

A Decision Fusion Framework for Treatment Recommendation Systems.

Science.gov (United States)

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

2015-01-01

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

Multimedia services in intelligent environments advances in recommender systems

CERN Document Server

Virvou, Maria; Jain, Lakhmi

2013-01-01

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

Application of principles of quality assurance recommended by ISO 9000 Standards to Regulatory Program

International Nuclear Information System (INIS)

Jova Sed, Luis Andres; Bilbao Alfonso, Alejandro Victor

2001-01-01

For several years, the necessity of applying the programs of quality assurance to the radiation protection activities has been highlighted; however there has been little progress in this direction, even in the philosophical and methodological development of the topic. The objective of this work is to transmit some recommendations of how the Regulatory Authorities of developing countries can organize the quality assurance system of their own activity, following the main precepts of the international standard series ISO 9000. In very tight synthesis it describes the importance that has the definition of a policy of quality for a Regulatory Authority, the quality objectives, the definition of the responsibilities and attributions in relation with quality assurance, and others elements of the ISO 9000, and how to apply it. (author)

A program optimization system for the cleanup of DOE hazardous waste sites an application to FY 1990 funding decisions

International Nuclear Information System (INIS)

Merkhofer, M.W.; Jenni, K.E.; Cotton, T.A.; Lehr, J.C.; Longo, T.P.

1989-01-01

This paper describes a formal system used by the Department of Energy (DOE) as an aid for allocating funds for cleaning up hazardous waste sites. The system, called the Program Optimization System (POS), is based on multiattribute utility analysis and was developed for DOE's Hazardous Waste and Remedial Actions Division (HWRAD). HWRAD has responsibility for recommending environmental restoration (ER) activities to the Assistant Secretary of Energy. Recently, the POS was used to analyze and recommend funding levels for FY 1990 cleanup activities at DOE defense program facilities

Improvement program of state supervision system for radioactive and nuclear installations

International Nuclear Information System (INIS)

Cardenas, J.

1993-01-01

The current program begins as part of a policy to take care of the development of the cuban nuclear program and with the objective of improving the state supervision system of nuclear and radioactive facilities on the basis of the national experience, good skills internationally accepted and taking into account IAEA recommendations. The program develops the following topics: reorientation and restructure of state supervision, review of the current nuclear legislature, update of regulations of facility safety and qualification and training of state supervision personnel

A recommender system for medical imaging diagnostic.

Science.gov (United States)

Monteiro, Eriksson; Valente, Frederico; Costa, Carlos; Oliveira, José Luís

2015-01-01

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.

Solving the apparent diversity-accuracy dilemma of recommender systems.

Science.gov (United States)

Zhou, Tao; Kuscsik, Zoltán; Liu, Jian-Guo; Medo, Matús; Wakeling, Joseph Rushton; Zhang, Yi-Cheng

2010-03-09

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.

Summary and recommendations of the NRC/INEL Activated Carbon Testing Program

International Nuclear Information System (INIS)

Scarpellino, C.D.; Sill, C.W.

1986-01-01

The Committee on Nuclear Air and Gas Treatment (CONAGT) of the American Society of Mechanical Engineers (ASME) sponsored an interlaboratory testing program, round-robin, of nuclear-grade activated carbon. The results of this round-robin revealed gross differences in penetration of radio-labeled methyl iodide as measured by the various laboratories when using Method A of the ASTM D-3803-79 Standard. These differences prompted the Nuclear Regulatory Commission (NRC) to establish the NRC/INEL Activated Carbon Testing Program to determine the causes of these discrepancies and to provide recommendations that could lead to an accurate and reliable testing procedure that would ensure an adequate method for assessing the capability of activated carbon to remove radioiodine from gas streams within commercial nuclear power plants. The NRC/INEL Activated Carbon Testing Program has conducted formal and informal interlaboratory comparisons to identify problems with the test method and its application and to assess the effectiveness of changes to procedures and equipment voluntarily implemented by commercial laboratories to mitigate the disparity of test results. The results of the first formal NRC/INEL Interlaboratory Comparison (IC) essentially verified the CONAGT round-robin results despite the use of a detailed test protocol. This data indicated that many of the participating laboratories probably had been operating outside the ASTM specifications for relative humidity (RH) and flow. In addition, this process provided information which was used to modify the testing protocol employed for the second NRC/INEL Interlaboratory Comparison (IC-2) to make it more rugged and reliable. These changes to the protocol together with the results of INEL sensitivity testing are the basis for the recommendations presented

Classification process in a text document recommender system

Directory of Open Access Journals (Sweden)

Dan MUNTEANU

2005-12-01

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.

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

Science.gov (United States)

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

2014-02-01

The acquisition and classification of scientific evidence, and subsequent formulation of recommendations constitute the basis for the development of clinical practice guidelines. There are several systems for the classification of evidence and strength of recommendations; the most commonly used nowadays is the Grading of Recommendations, Assessment, Development and Evaluation system (GRADE). The GRADE system initially classifies the evidence into high or low, coming from experimental or observational studies; subsequently and following a series of considerations, the evidence is classified into high, moderate, low or very low. The strength of recommendations is based not only on the quality of the evidence, but also on a series of factors such as the risk/benefit balance, values and preferences of the patients and professionals, and the use of resources or costs. Copyright © 2013 AEC. Published by Elsevier Espana. All rights reserved.

Mining the preferences of patients for ubiquitous clinic recommendation.

Science.gov (United States)

Chen, Tin-Chih Toly; Chiu, Min-Chi

2018-03-06

A challenge facing all ubiquitous clinic recommendation systems is that patients often have difficulty articulating their requirements. To overcome this problem, a ubiquitous clinic recommendation mechanism was designed in this study by mining the clinic preferences of patients. Their preferences were defined using the weights in the ubiquitous clinic recommendation mechanism. An integer nonlinear programming problem was solved to tune the values of the weights on a rolling basis. In addition, since it may take a long time to adjust the values of weights to their asymptotic values, the back propagation network (BPN)-response surface method (RSM) method is applied to estimate the asymptotic values of weights. The proposed methodology was tested in a regional study. Experimental results indicated that the ubiquitous clinic recommendation system outperformed several existing methods in improving the successful recommendation rate.

Total System Performance Assessment for the Site Recommendation

Energy Technology Data Exchange (ETDEWEB)

None

2000-10-02

As mandated in the Nuclear Waste Policy Act of 1982, the U.S. Department of Energy (DOE) has been investigating a candidate site at Yucca Mountain, Nevada, to determine whether it is suitable for development of the nation's first repository for permanent geologic disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW). The Nuclear Waste Policy Amendments Act of 1987 directed that only Yucca Mountain be characterized to evaluate the site's suitability. Three main components of the DOE site characterization program are testing, design, and performance assessment. These program components consist of: Investigation of natural features and processes by analyzing data collected from field tests conducted above and below ground and from laboratory tests of rock, gas, and water samples Design of a repository and waste packages tailored to the site features, supported by laboratory testing of candidate materials for waste packages and design related testing in the underground tunnels where waste would be emplaced Quantitative estimates of the performance of the total repository system, over a range of possible conditions and for different repository configurations, by means of computer modeling techniques that are based on site and materials testing data and accepted principles of physics and chemistry. To date, DOE has completed and documented four major iterations of total system performance assessment (TSPA) for the Yucca Mountain site: TSPA-91 (Barnard et al. 1992), TSPA-93 (Wilson et al. 1994; CRWMS M and O 1994), TSPA-95 (CRWMS M and O 1995), and the Total System Performance Assessment-Viability Assessment (TSPA-VA) (DOE 1998a, Volume 3). Each successive TSPA iteration has advanced the technical understanding of the performance attributes of the natural features and processes and enhanced engineering designs. The next major iteration of TSPA is to be conducted in support of the next major programmatic milestone for the DOE, namely the

Efficient privacy-enhanced familiarity-based recommender system

NARCIS (Netherlands)

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

Music recommendation system for biofied building considering multiple residents

Science.gov (United States)

Ito, Takahiro; Mita, Akira

2012-04-01

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

Geochemistry Review Panel report on the SRP geochemistry program and draft geochemistry summary program plan (May, 1986) and discussion of panel recommendations

International Nuclear Information System (INIS)

1986-12-01

The Geochemistry Review Panel (GRP) was established by the Salt Repository Project Office (SRPO) to help evaluate geochemistry-related issues in the US Department of Energy's nuclear waste repository program. The May 1986 meeting of the GRP reviewed the Salt Repository Program (SRP) geochemistry program developed by the Office of Nuclear Waste Isolation (ONWI). This program is described in the Draft Geochemistry Plan of April 9, 1986. This report documents the GRP's comments and recommendations on this subject and the ONWI responses to the specific points raised by the GRP

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

Science.gov (United States)

Wang, Wei; Wang, Hongwei

2015-10-01

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

System programming languages

OpenAIRE

Šmit, Matej

2016-01-01

Most operating systems are written in the C programming language. Similar is with system software, for example, device drivers, compilers, debuggers, disk checkers, etc. Recently some new programming languages emerged, which are supposed to be suitable for system programming. In this thesis we present programming languages D, Go, Nim and Rust. We defined the criteria which are important for deciding whether programming language is suitable for system programming. We examine programming langua...

Recommended Practice: Creating Cyber Forensics Plans for Control Systems

Energy Technology Data Exchange (ETDEWEB)

Eric Cornelius; Mark Fabro

2008-08-01

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

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

Science.gov (United States)

Ravi, Logesh; Vairavasundaram, Subramaniyaswamy

2016-01-01

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

Ranking and Context-awareness in Recommender Systems

NARCIS (Netherlands)

Shi, Y.

2013-01-01

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

Recommender System for Sales at Material Store Using Fuzzy Tsukamoto

Directory of Open Access Journals (Sweden)

July Kurniawan

2016-02-01

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

Waves in Seagrass Systems: Review and Technical Recommendations

Science.gov (United States)

2006-11-01

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

A Conceptual Framework for Evolving, Recommender Online Learning Systems

Science.gov (United States)

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

2012-01-01

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

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

NARCIS (Netherlands)

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

2013-01-01

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

Enabling Open Research Data Discovery through a Recommender System

Science.gov (United States)

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

2017-04-01

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

Universities Earth System Scientists Program

Science.gov (United States)

Estes, John E.

1995-01-01

This document constitutes the final technical report for the National Aeronautics and Space Administration (NASA) Grant NAGW-3172. This grant was instituted to provide for the conduct of research under the Universities Space Research Association's (USRA's) Universities Earth System Scientist Program (UESSP) for the Office of Mission to Planet Earth (OMTPE) at NASA Headquarters. USRA was tasked with the following requirements in support of the Universities Earth System Scientists Programs: (1) Bring to OMTPE fundamental scientific and technical expertise not currently resident at NASA Headquarters covering the broad spectrum of Earth science disciplines; (2) Conduct basic research in order to help establish the state of the science and technological readiness, related to NASA issues and requirements, for the following, near-term, scientific uncertainties, and data/information needs in the areas of global climate change, clouds and radiative balance, sources and sinks of greenhouse gases and the processes that control them, solid earth, oceans, polar ice sheets, land-surface hydrology, ecological dynamics, biological diversity, and sustainable development; (3) Evaluate the scientific state-of-the-field in key selected areas and to assist in the definition of new research thrusts for missions, including those that would incorporate the long-term strategy of the U.S. Global Change Research Program (USGCRP). This will, in part, be accomplished by study and evaluation of the basic science needs of the community as they are used to drive the development and maintenance of a global-scale observing system, the focused research studies, and the implementation of an integrated program of modeling, prediction, and assessment; and (4) Produce specific recommendations and alternative strategies for OMTPE that can serve as a basis for interagency and national and international policy on issues related to Earth sciences.

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

Directory of Open Access Journals (Sweden)

Logesh Ravi

2016-01-01

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

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

Science.gov (United States)

Ravi, Logesh; Vairavasundaram, Subramaniyaswamy

2016-01-01

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented. PMID:27069468

Development of a Recommender System based on Personal History

Science.gov (United States)

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.

a Context-Aware Tourism Recommender System Based on a Spreading Activation Method

Science.gov (United States)

Bahramian, Z.; Abbaspour, R. Ali; Claramunt, C.

2017-09-01

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.

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

Directory of Open Access Journals (Sweden)

Z. Bahramian

2017-09-01

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

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

NARCIS (Netherlands)

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

2013-01-01

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

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

NARCIS (Netherlands)

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

2013-01-01

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

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

Science.gov (United States)

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

2017-02-01

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

Collaborative User Network Embedding for Social Recommender Systems

KAUST Repository

Zhang, Chuxu

2017-06-09

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

The power of ground user in recommender systems.

Directory of Open Access Journals (Sweden)

Yanbo Zhou

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.

The power of ground user in recommender systems.

Science.gov (United States)

Zhou, Yanbo; Lü, Linyuan; Liu, Weiping; Zhang, Jianlin

2013-01-01

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.

User Controllability in a Hybrid Recommender System

Science.gov (United States)

Parra Santander, Denis Alejandro

2013-01-01

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

On the Recommender System for University Library

Science.gov (United States)

Fu, Shunkai; Zhang, Yao; Seinminn

2013-01-01

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)…

Mining Feedback in Ranking and Recommendation Systems

Science.gov (United States)

Zhuang, Ziming

2009-01-01

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

Evaluation of Recommender Systems for Technology-Enhanced Learning: Challenges and Possible Solutions

NARCIS (Netherlands)

Sandy, Heleau; Drachsler, Hendrik; Gillet, Dennis

2009-01-01

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,

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

Science.gov (United States)

Alphy, Anna; Prabakaran, S

2015-01-01

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

A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System

Science.gov (United States)

Geetha, G.; Safa, M.; Fancy, C.; Saranya, D.

2018-04-01

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.

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

Directory of Open Access Journals (Sweden)

Shereen H. Ali

2016-03-01

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

Kernel based collaborative recommender system for e-purchasing

Indian Academy of Sciences (India)

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.

At-Risk Programs for Middle School and High School: Essential Components and Recommendations for Administrators and Teachers.

Science.gov (United States)

Bateman, Susan; Karr-Kidwell, PJ

This paper provides an extensive literature review concerning at-risk students and their needs, identifies the essential components necessary for effective at-risk programming, and describes successful at-risk programs and recommendations for administrators and teachers at the middle- and high-school levels. The literature review presents research…

Achieving Optimal Privacy in Trust-Aware Social Recommender Systems

Science.gov (United States)

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.

Preference Elicitation and Negotiation in a Group Recommender System

OpenAIRE

Álvarez Márquez , Jesús ,; Ziegler , Jurgen

2015-01-01

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...

Analysis of PGY-1 Pharmacy Resident Candidate Letters of Recommendation at an Academically Affiliated Residency Program.

Science.gov (United States)

McLaughlin, Milena M; Masic, Dalila; Gettig, Jacob P

2018-04-01

Letters of recommendation (LORs) are a critical component for differentiating among similarly qualified pharmacy residency candidates. These letters contain information that is difficult to ascertain from curricula vitae and pharmacy school transcripts. LOR writers may use any words or phrases appropriate for each candidate as there is no set framework for LORs. The objective of this study was to characterize descriptive themes in postgraduate year 1 (PGY-1) pharmacy residency candidates' LORs and to examine which themes of PGY-1 pharmacy residency candidates' LORs are predictive of an interview invitation at an academically affiliated residency program. LORs for candidates from the Pharmacy Online Residency Centralized Application System (PhORCAS) from 2013 and 2014 for the Midwestern University PGY-1 Pharmacy Residency were analyzed. LOR characteristics and descriptive themes were collected. All scores for candidate characteristics and overall PhORCAS recommendation were also recorded. A total of 351 LORs for 111 candidates from 2013 (n = 47 candidates) and 2014 (n = 64 candidates) were analyzed; 36 (32.4%) total candidates were offered an interview. Themes that were identified as predictors of an interview included a higher median (interquartile range) number of standout words (3 words [1.3-4] vs 3.8 words [2.5-5.5], P < .01) and teaching references (3.7 words [2.7-6] vs 5.7 words [3.7-7.8], P = .01). For this residency program, standout words and teaching references were important when offering interviews.

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

Science.gov (United States)

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

2014-01-01

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

Nuclear proliferation and civilian nuclear power: report of the Nonproliferation Alternative Systems Assessment Program. Volume 1. Program summary

Energy Technology Data Exchange (ETDEWEB)

1979-12-01

This report summarizes the Nonproliferation Alternative Systems Assessment Program (NASAP): its background, its studies, and its results. This introductory chapter traces the growth of the issue of nuclear weapons proliferation and the organization and objectives of NASAP. Chapter 2 summarizes the program's assessments, findings and recommendations. Each of Volumes II-VII reports on an individual assessment (Volume II: Proliferation Resistance; Volume III: Resources and Fuel Cycle Facilities; Volume IV: Commercial Potential; Volume V: Economics and Systems Analysis; Volume VI: Safety and Environmental Considerations for Licensing; Volume VII: International Perspectives). Volume VIII (Advanced Concepts) presents a combined assessment of several less fully developed concepts, and Volume IX (Reactor and Fuel Cycle Descriptions) provides detailed descriptions of the reactor and fuel-cycle systems studied by NASAP.

Nuclear proliferation and civilian nuclear power: report of the Nonproliferation Alternative Systems Assessment Program. Volume 1. Program summary

International Nuclear Information System (INIS)

1979-12-01

This report summarizes the Nonproliferation Alternative Systems Assessment Program (NASAP): its background, its studies, and its results. This introductory chapter traces the growth of the issue of nuclear weapons proliferation and the organization and objectives of NASAP. Chapter 2 summarizes the program's assessments, findings and recommendations. Each of Volumes II-VII reports on an individual assessment (Volume II: Proliferation Resistance; Volume III: Resources and Fuel Cycle Facilities; Volume IV: Commercial Potential; Volume V: Economics and Systems Analysis; Volume VI: Safety and Environmental Considerations for Licensing; Volume VII: International Perspectives). Volume VIII (Advanced Concepts) presents a combined assessment of several less fully developed concepts, and Volume IX (Reactor and Fuel Cycle Descriptions) provides detailed descriptions of the reactor and fuel-cycle systems studied by NASAP

Nuclear proliferation and civilian nuclear power. Report of the Nonproliferation Alternative Systems Assessment Program. Volume I. Program summary

International Nuclear Information System (INIS)

1980-06-01

This report summarizes the Nonproliferation Alternative Systems Assessment Program (NASAP): its background, its studies, and its results. The introductory chapter traces the growth of the issue of nuclear weapons proliferation and the organization and objectives of NASAP. Chapter 2 summarizes the program's assessments, findings, and recommendations. Each of Volumes II-VII reports on an individual assessment (Volumn II: Proliferation Resistance; Volume III: Resources and Fuel Cycle Facilities; Volume IV: Commercial Potential; Volume V: Economics and Systems Analysis; Volume VI: Safety and Environmental Considerations for Licensing; Volume VII: International Perspectives). Volume VIII (Advanced Concepts) presents a combined assessment of several less fully developed concepts, and Volume IX (Reactor and Fuel Cycle Descriptions) provides detailed descriptions of the reactor and fuel-cycle systems studied by NASAP

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

Science.gov (United States)

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

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

Teacher Efficacy and Disproportional Special Education Recommendations

Science.gov (United States)

Branscombe, Peter

2017-01-01

According to literature, African American male students are disproportionately placed in special education programs throughout our national public school systems. Therefore, this study was intended to examine factors that may influence a teacher's decision to recommend students for special education services. The target population for this study…

Selecting the recommended waste management system for the midwest compact

International Nuclear Information System (INIS)

Sutherland, A.A.; Robertson, B.C.; Drobny, N.L.

1987-01-01

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

Assessment of Primary Representational Systems with Neurolinguistic Programming: Examination of Preliminary Literature.

Science.gov (United States)

Dorn, Fred J.; And Others

1983-01-01

Reviews the inconsistent findings of studies on neurolinguistic programing and recommends some areas that should be examined to verify various claims. Discusses methods of assessing client's primary representational systems, including predicate usage and eye movements, and suggests that more reliable methods of assessing PRS must be found. (JAC)

Evaluation of Neighbourhood Selection Methods in Decentralized Recommendation Systems

NARCIS (Netherlands)

M. Clements (Maarten); A.P. de Vries (Arjen); J.A. Pouwelse; J. Wang (Jun); M.J.T. Reinders

2007-01-01

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

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

Directory of Open Access Journals (Sweden)

Paula Andrea RODRÍGUEZ MARÍN

2015-12-01

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

Preparation of a program for the independent verification of the brachytherapy planning systems calculations

International Nuclear Information System (INIS)

V Carmona, V.; Perez-Calatayud, J.; Lliso, F.; Richart Sancho, J.; Ballester, F.; Pujades-Claumarchirant, M.C.; Munoz, M.

2010-01-01

In this work a program is presented that independently checks for each patient the treatment planning system calculations in low dose rate, high dose rate and pulsed dose rate brachytherapy. The treatment planning system output text files are automatically loaded in this program in order to get the source coordinates, the desired calculation point coordinates and the dwell times when it is the case. The source strength and the reference dates are introduced by the user. The program allows implementing the recommendations about independent verification of the clinical brachytherapy dosimetry in a simple and accurate way, in few minutes. (Author).

Canadian solar export market study. Export policy recommendations

Energy Technology Data Exchange (ETDEWEB)

1983-11-01

This report outlines policies and recommendations on the export of Canadian solar equipment and technology, with a view toward stimulating the domestic solar industry. The current picture is of an industry which is relatively small, operates in a competitive domestic market with low profit margins, and needs assistance in order to break into the world market. A number of recommendations are therefore made on the main thrust of industry and government solar export development activities. An export development program is described which includes a strategy of concentrating on a limited number of product lines, namely: low-temperature solar heating systems for recreational applications, integrated residential water heating systems, prepackaged commercial water heating systems, and industrial pre-heat systems. It is also recommended that this strategy be directed only at a limited number of target countries where the market justifies such activity. Market research, international cooperation agreements, promotional services, and proper export organization are also needed.

Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds

Science.gov (United States)

Seko, Atsuto; Hayashi, Hiroyuki; Kashima, Hisashi; Tanaka, Isao

2018-01-01

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.

InterTechnology Corporation proposed criteria and recommendations for selection of PON non-residential demonstration sites

Energy Technology Data Exchange (ETDEWEB)

None

1976-12-01

This report has been prepared with the objective of providing ERDA with recommended procedures for implementing the strategies set forth in the Systems Level Plan which are considered essential to the success of the National Demonstration Program. In order to logically develop these recommendations, this report is divided into three sections: A. Overview of the Demonstration Program to date. B. Essential Overall Program Strategies. C. Candidate Screening and Selection Criteria. (WDM)

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

Science.gov (United States)

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

2014-01-01

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

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

Science.gov (United States)

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

2015-01-01

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

Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

Directory of Open Access Journals (Sweden)

Zhengyou Xia

2014-01-01

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

Apollo experience report: Guidance and control systems. Engineering simulation program

Science.gov (United States)

Gilbert, D. W.

1973-01-01

The Apollo Program experience from early 1962 to July 1969 with respect to the engineering-simulation support and the problems encountered is summarized in this report. Engineering simulation in support of the Apollo guidance and control system is discussed in terms of design analysis and verification, certification of hardware in closed-loop operation, verification of hardware/software compatibility, and verification of both software and procedures for each mission. The magnitude, time, and cost of the engineering simulations are described with respect to hardware availability, NASA and contractor facilities (for verification of the command module, the lunar module, and the primary guidance, navigation, and control system), and scheduling and planning considerations. Recommendations are made regarding implementation of similar, large-scale simulations for future programs.

Recommendations for scale-up of community-based misoprostol distribution programs.

Science.gov (United States)

Robinson, Nuriya; Kapungu, Chisina; Carnahan, Leslie; Geller, Stacie

2014-06-01

Community-based distribution of misoprostol for prevention of postpartum hemorrhage (PPH) in resource-poor settings has been shown to be safe and effective. However, global recommendations for prenatal distribution and monitoring within a community setting are not yet available. In order to successfully translate misoprostol and PPH research into policy and practice, several critical points must be considered. A focus on engaging the community, emphasizing the safe nature of community-based misoprostol distribution, supply chain management, effective distribution, coverage, and monitoring plans are essential elements to community-based misoprostol program introduction, expansion, or scale-up. Copyright © 2014 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Business Processes Modeling Recommender Systems: User Expectations and Empirical Evidence

Directory of Open Access Journals (Sweden)

Michael Fellmann

2018-04-01

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.

Supporting Multi-Agent Reputation Calculation in the Wikipedia Recommender System

DEFF Research Database (Denmark)

Jensen, Christian D.

2010-01-01

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...

DIETOS: A dietary recommender system for chronic diseases monitoring and management.

Science.gov (United States)

Agapito, Giuseppe; Simeoni, Mariadelina; Calabrese, Barbara; Caré, Ilaria; Lamprinoudi, Theodora; Guzzi, Pietro H; Pujia, Arturo; Fuiano, Giorgio; Cannataro, Mario

2018-01-01

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

The Impact of Recommender Systems on Item-, User-, and Rating-Diversity

NARCIS (Netherlands)

Kowalczyk, W.J.; Szlavik, Z.; Schut, M.C.

2012-01-01

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

Preliminary recommendations on the design of the characterization program for the Hanford Site single-shell tanks: A system analysis

International Nuclear Information System (INIS)

Buck, J.W.; Peffers, M.S.; Hwang, S.T.

1991-11-01

The work described in this volume was conducted by Pacific Northwest Laboratory to provide preliminary recommendations on data quality objectives (DQOs) to support the Waste Characterization Plan (WCP) and closure decisions for the Hanford Site single-shell tanks (SSTs). The WCP describes the first of a two-phase characterization program that will obtain information to assess and implement disposal options for SSTs. This work was performed for the Westinghouse Hanford Company (WHC), the current operating contractor on the Hanford Site. The preliminary DQOs contained in this volume deal with the analysis of SST wastes in support of the WCP and final closure decisions. These DQOs include information on significant contributors and detection limit goals (DLGs) for SST analytes based on public health risk

Certainty in Stockpile Computing: Recommending a Verification and Validation Program for Scientific Software

Energy Technology Data Exchange (ETDEWEB)

Lee, J.R.

1998-11-01

As computing assumes a more central role in managing the nuclear stockpile, the consequences of an erroneous computer simulation could be severe. Computational failures are common in other endeavors and have caused project failures, significant economic loss, and loss of life. This report examines the causes of software failure and proposes steps to mitigate them. A formal verification and validation program for scientific software is recommended and described.

Impact of a pharmacy technician-centered medication reconciliation program on medication discrepancies and implementation of recommendations.

Science.gov (United States)

Kraus, Sarah K; Sen, Sanchita; Murphy, Michelle; Pontiggia, Laura

2017-01-01

To evaluate the impact of a pharmacy-technician centered medication reconciliation (PTMR) program by identifying and quantifying medication discrepancies and outcomes of pharmacist medication reconciliation recommendations. A retrospective chart review was performed on two-hundred patients admitted to the internal medicine teaching services at Cooper University Hospital in Camden, NJ. Patients were selected using a stratified systematic sample approach and were included if they received a pharmacy technician medication history and a pharmacist medication reconciliation at any point during their hospital admission. Pharmacist identified medication discrepancies were analyzed using descriptive statistics, bivariate analyses. Potential risk factors were identified using multivariate analyses, such as logistic regression and CART. The priority level of significance was set at 0.05. Three-hundred and sixty-five medication discrepancies were identified out of the 200 included patients. The four most common discrepancies were omission (64.7%), non-formulary omission (16.2%), dose discrepancy (10.1%), and frequency discrepancy (4.1%). Twenty-two percent of pharmacist recommendations were implemented by the prescriber within 72 hours. A PTMR program with dedicated pharmacy technicians and pharmacists identifies many medication discrepancies at admission and provides opportunities for pharmacist reconciliation recommendations.

Impact of a pharmacy technician-centered medication reconciliation program on medication discrepancies and implementation of recommendations

Directory of Open Access Journals (Sweden)

Kraus SK

2017-06-01

Full Text Available Objectives: To evaluate the impact of a pharmacy-technician centered medication reconciliation (PTMR program by identifying and quantifying medication discrepancies and outcomes of pharmacist medication reconciliation recommendations. Methods: A retrospective chart review was performed on two-hundred patients admitted to the internal medicine teaching services at Cooper University Hospital in Camden, NJ. Patients were selected using a stratified systematic sample approach and were included if they received a pharmacy technician medication history and a pharmacist medication reconciliation at any point during their hospital admission. Pharmacist identified medication discrepancies were analyzed using descriptive statistics, bivariate analyses. Potential risk factors were identified using multivariate analyses, such as logistic regression and CART. The priority level of significance was set at 0.05. Results: Three-hundred and sixty-five medication discrepancies were identified out of the 200 included patients. The four most common discrepancies were omission (64.7%, non-formulary omission (16.2%, dose discrepancy (10.1%, and frequency discrepancy (4.1%. Twenty-two percent of pharmacist recommendations were implemented by the prescriber within 72 hours. Conclusion: A PTMR program with dedicated pharmacy technicians and pharmacists identifies many medication discrepancies at admission and provides opportunities for pharmacist reconciliation recommendations.

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

KAUST Repository

Liu, Xiao

2017-03-21

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

Recruitment recommendation system based on fuzzy measure and indeterminate integral

Science.gov (United States)

Yin, Xin; Song, Jinjie

2017-08-01

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

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

Science.gov (United States)

Williams, Craig H.

2004-01-01

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

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

Directory of Open Access Journals (Sweden)

Murale Narayanan

2016-03-01

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

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

Science.gov (United States)

Bambini, Riccardo; Cremonesi, Paolo; Turrin, Roberto

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

N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.

Science.gov (United States)

Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang

2014-01-01

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.

RecSys'17 joint workshop on interfaces and human decision making for recommender systems

NARCIS (Netherlands)

Brusilovsky, Peter; De Gemmis, Marco; Felfernig, Alexander; Lops, Pasquale; O'Donovan, John; Tintarev, Nava; Willemsen, Martijn

2017-01-01

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

RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

NARCIS (Netherlands)

Brusilovsky, P.; Felfernig, A.; Lops, P.; O'Donovan, J.; Semeraro, G.; Tintarev, N.; Willemsen, M.C.

2016-01-01

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

Personalised news filtering and recommendation system using Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model

Science.gov (United States)

Adeniyi, D. A.; Wei, Z.; Yang, Y.

2017-10-01

Recommendation problem has been extensively studied by researchers in the field of data mining, database and information retrieval. This study presents the design and realisation of an automated, personalised news recommendations system based on Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model. The proposed χ2SB-KNN model has the potential to overcome computational complexity and information overloading problems, reduces runtime and speeds up execution process through the use of critical value of χ2 distribution. The proposed recommendation engine can alleviate scalability challenges through combined online pattern discovery and pattern matching for real-time recommendations. This work also showcases the development of a novel method of feature selection referred to as Data Discretisation-Based feature selection method. This is used for selecting the best features for the proposed χ2SB-KNN algorithm at the preprocessing stage of the classification procedures. The implementation of the proposed χ2SB-KNN model is achieved through the use of a developed in-house Java program on an experimental website called OUC newsreaders' website. Finally, we compared the performance of our system with two baseline methods which are traditional Euclidean distance K-nearest neighbour and Naive Bayesian techniques. The result shows a significant improvement of our method over the baseline methods studied.

The Effect of Incorporating Good Learners' Ratings in e-Learning Content-Based Recommender System

Science.gov (United States)

Ghauth, Khairil Imran; Abdullah, Nor Aniza

2011-01-01

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…

Advanced energy design and operation technologies research: Recommendations for a US Department of Energy multiyear program plan

Energy Technology Data Exchange (ETDEWEB)

Brambley, M.R.; Crawley, D.B.; Hostetler, D.D.; Stratton, R.C.; Addision, M.S.; Deringer, J.J.; Hall, J.D.; Selkowitz, S.E.

1988-12-01

This document describes recommendations for a multiyear plan developed for the US Department of Energy (DOE) as part of the Advanced Energy Design and Operation Technologies (AEDOT) project. The plan is an outgrowth of earlier planning activities conducted for DOE as part of design process research under the Building System Integration Program (BSIP). The proposed research will produce intelligent computer-based design and operation technologies for commercial buildings. In this document, the concept is explained, the need for these new computer-based environments is discussed, the benefits are described, and a plan for developing the AEDOT technologies is presented for the 9-year period beginning FY 1989. 45 refs., 37 figs., 9 tabs.

Recommendation for sanitary waste water system replacement, 222-S

International Nuclear Information System (INIS)

Simmons, F.M.

1994-01-01

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

Earth Sciences Data and Information System (ESDIS) program planning and evaluation methodology development

Science.gov (United States)

Dickinson, William B.

1995-01-01

An Earth Sciences Data and Information System (ESDIS) Project Management Plan (PMP) is prepared. An ESDIS Project Systems Engineering Management Plan (SEMP) consistent with the developed PMP is also prepared. ESDIS and related EOS program requirements developments, management and analysis processes are evaluated. Opportunities to improve the effectiveness of these processes and program/project responsiveness to requirements are identified. Overall ESDIS cost estimation processes are evaluated, and recommendations to improve cost estimating and modeling techniques are developed. ESDIS schedules and scheduling tools are evaluated. Risk assessment, risk mitigation strategies and approaches, and use of risk information in management decision-making are addressed.

Promoting Mental Health in Unaccompanied Refugee Minors: Recommendations for Primary Support Programs

Science.gov (United States)

El-Awad, Usama; Petermann, Franz; Reinelt, Tilman

2017-01-01

During the last years, the number of refugees around the world increased to about 22.5 million. The mental health of refugees, especially of unaccompanied minors (70% between the ages of 16 and 18 years) who have been exposed to traumatic events (e.g., war), is generally impaired with symptoms of post-traumatic stress disorder, depression, and anxiety. Several studies revealed (1) a huge variation among the prevalence rates of these mental problems, and (2) that post-migration stressors (e.g., language barriers, cultural differences) might be at least as detrimental to mental health as the traumatic events in pre- and peri-flight. As psychotherapy is a limited resource that should be reserved for severe cases and as language trainings are often publicly offered for refugees, we recommend focusing on intercultural competence, emotion regulation, and goal setting and goal striving in primary support programs: Intercultural competence fosters adaptation by giving knowledge about cultural differences in values and norms. Emotion regulation regarding empathy, positive reappraisal, and cultural differences in emotion expression fosters both adaptation and mental health. Finally, supporting unaccompanied refugee minors in their goal setting and goal striving is necessary, as they carry many unrealistic wishes and unattainable goals, which can be threatening to their mental health. Building on these three psychological processes, we provide recommendations for primary support programs for unaccompanied refugee minors that are aged 16 to 18 years. PMID:29104237

Promoting Mental Health in Unaccompanied Refugee Minors: Recommendations for Primary Support Programs

Directory of Open Access Journals (Sweden)

Usama El-Awad

2017-11-01

Full Text Available During the last years, the number of refugees around the world increased to about 22.5 million. The mental health of refugees, especially of unaccompanied minors (70% between the ages of 16 and 18 years who have been exposed to traumatic events (e.g., war, is generally impaired with symptoms of post-traumatic stress disorder, depression, and anxiety. Several studies revealed (1 a huge variation among the prevalence rates of these mental problems, and (2 that post-migration stressors (e.g., language barriers, cultural differences might be at least as detrimental to mental health as the traumatic events in pre- and peri-flight. As psychotherapy is a limited resource that should be reserved for severe cases and as language trainings are often publicly offered for refugees, we recommend focusing on intercultural competence, emotion regulation, and goal setting and goal striving in primary support programs: Intercultural competence fosters adaptation by giving knowledge about cultural differences in values and norms. Emotion regulation regarding empathy, positive reappraisal, and cultural differences in emotion expression fosters both adaptation and mental health. Finally, supporting unaccompanied refugee minors in their goal setting and goal striving is necessary, as they carry many unrealistic wishes and unattainable goals, which can be threatening to their mental health. Building on these three psychological processes, we provide recommendations for primary support programs for unaccompanied refugee minors that are aged 16 to 18 years.

A System for Recommending Rental Properties

Directory of Open Access Journals (Sweden)

Bernard Shibwabo Kasamani

2017-07-01

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

Modeling Temporal Bias of Uplift Events in Recommender Systems

KAUST Repository

Altaf, Basmah

2013-01-01

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

Evaluation of an inpatient psychiatric hospital physician education program and adherence to American Diabetes Association practice recommendations.

Science.gov (United States)

Koffarnus, Robin L; Mican, Lisa M; Lopez, Debra A; Barner, Jamie C

2016-03-01

This study evaluated adherence to American Diabetes Association (ADA) recommendations for diabetes monitoring following an educational intervention for physicians in an inpatient psychiatric hospital. This retrospective chart review was conducted in an inpatient psychiatric institution from July 1, 2010-January 15, 2011. A total of 120 subjects (60 subjects each in the pre- and post-intervention groups) meeting the inclusion criteria served as the study sample. Included subjects were admitted and discharged from an inpatient psychiatric institution within 90 days prior to (pre-intervention) and following (post-intervention) the physician education program. The medical staff was presented an educational program intervention, consisting of a 30 minute overview of the ADA 2010 Standards of Care recommendations and distribution of laminated treatment reminders. Electronic grouped order sets for patients with diabetes were also created and implemented. The primary outcome was change (pre-intervention to post-intervention) in frequency of hemoglobin A1c documentation on admission following the intervention. Secondary outcomes included the change in frequency of documentation of fasting plasma glucose, serum creatinine, urine creatinine/microalbumin ratio (UMA), fasting lipid profile (FLP), and change in days on sliding scale insulin. Regarding change in frequency of documentation of A1c values on admission, chi-square analysis revealed a significant increase from pre-intervention to post-intervention period of 30% (n = 18) to 61.7% (n = 37), respectively (p = 0.0005). Documentation of FLP also significantly increased [73.3% vs. 91.7% (p = 0.0082)]. There were no significant differences in the documentation of fasting plasma glucose, serum creatinine, and UMA or days treated with sliding scale insulin. The physician education program was successful in increasing the assessment of A1c values and lipid profiles for patients with diabetes mellitus in a psychiatric

Combination of evidence in recommendation systems characterized by distance functions

Energy Technology Data Exchange (ETDEWEB)

Rocha, L. M. (Luis Mateus)

2002-01-01

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

A Study on Recommendation Systems in Location Based Social Networking

Directory of Open Access Journals (Sweden)

Ranganath Ashok Kumar

2017-01-01

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.

Changing Paradigms: From Schooling to Schools as Adaptive Recommendation Systems

Science.gov (United States)

Petersen, Anne Kristine; Christiansen, Rene B.; Gynther, Karsten

2017-01-01

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…

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.

Science.gov (United States)

Sadasivam, Rajani Shankar; Cutrona, Sarah L; Kinney, Rebecca L; Marlin, Benjamin M; Mazor, Kathleen M; Lemon, Stephenie C; Houston, Thomas K

2016-03-07

What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.

Which Doctor to Trust: A Recommender System for Identifying the Right Doctors.

Science.gov (United States)

Guo, Li; Jin, Bo; Yao, Cuili; Yang, Haoyu; Huang, Degen; Wang, Fei

2016-07-07

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.

A fuzzy recommendation system for daily water intake

OpenAIRE

Bin Dai; Rung-Ching Chen; Shun-Zhi Zhu; Chung-Yi Huang

2016-01-01

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...

PRACA Enhancement Pilot Study Report: Engineering for Complex Systems Program (formerly Design for Safety), DFS-IC-0006

Science.gov (United States)

Korsmeyer, David; Schreiner, John

2002-01-01

This technology evaluation report documents the findings and recommendations of the Engineering for Complex Systems Program (formerly Design for Safety) PRACA Enhancement Pilot Study of the Space Shuttle Program's (SSP's) Problem Reporting and Corrective Action (PRACA) System. A team at NASA Ames Research Center (ARC) performed this Study. This Study was initiated as a follow-on to the NASA chartered Shuttle Independent Assessment Team (SIAT) review (performed in the Fall of 1999) which identified deficiencies in the current PRACA implementation. The Pilot Study was launched with an initial qualitative assessment and technical review performed during January 2000 with the quantitative formal Study (the subject of this report) started in March 2000. The goal of the PRACA Enhancement Pilot Study is to evaluate and quantify the technical aspects of the SSP PRACA systems and recommend enhancements to address deficiencies and in preparation for future system upgrades.

A Real-Time Taxicab Recommendation System Using Big Trajectories Data

Directory of Open Access Journals (Sweden)

Pengpeng Chen

2017-01-01

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

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

Science.gov (United States)

Ramezani, Mohsen; Yaghmaee, Farzin

2016-09-01

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

A Context Aware Recommender System for Mobile Phone Selection Using Combination of Elimination Method and Analytic Hierarchy Processing

Directory of Open Access Journals (Sweden)

Jalal Rezaeenour

2017-09-01

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.

Value of Recommendation Systems for Online Investors

OpenAIRE

Rustam Vahidov; Raafat Saade; Ahmed Eldiwany

2012-01-01

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...

Compositional descriptor-based recommender system for the materials discovery

Science.gov (United States)

Seko, Atsuto; Hayashi, Hiroyuki; Tanaka, Isao

2018-06-01

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.

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

Science.gov (United States)

Su, Chung-Ho

2017-01-01

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

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

Science.gov (United States)

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

2015-12-01

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

A Fast Recommender System for Cold User Using Categorized Items

Directory of Open Access Journals (Sweden)

Hamid Jazayeriy

2018-01-01

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.

Recommended safety, reliability, quality assurance and management aerospace techniques with possible application by the DOE to the high-level radioactive waste repository program

International Nuclear Information System (INIS)

Bland, W.M. Jr.

1985-05-01

Aerospace SRQA and management techniques, principally those developed and used by the NASA Lyndon B. Johnson Space Center on the manned space flight programs, have been assessed for possible application by the DOE and the DOE-contractors to the high level radioactive waste repository program that results from the implementation of the NWPA of 1982. Those techniques believed to have the greatest potential for usefulness to the DOE and the DOE-contractors have been discussed in detail and are recommended to the DOE for adoption; discussion is provided for the manner in which this transfer of technology can be implemented. Six SRQA techniques and two management techniques are recommended for adoption by the DOE; included with the management techniques is a recommendation for the DOE to include a licensing interface with the NRC in the application of the milestone reviews technique. Three other techniques are recommended for study by the DOE for possible adaptation to the DOE program

Earth Observatory Satellite system definition study. Report 4: Low cost management approach and recommendations

Science.gov (United States)

1974-01-01

An analysis of low cost management approaches for the development of the Earth Observatory Satellite (EOS) is presented. The factors of the program which tend to increase costs are identified. The NASA/Industry interface is stressed to show how the interface can be improved to produce reduced program costs. Techniques and examples of cost reduction which can be applied to the EOS program are tabulated. Specific recommendations for actions to be taken to reduce costs in prescribed areas are submitted.

Dataset-driven research for improving recommender systems for learning

NARCIS (Netherlands)

Verbert, Katrien; Drachsler, Hendrik; Manouselis, Nikos; Wolpers, Martin; Vuorikari, Riina; Duval, Erik

2011-01-01

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,

Recommended Practice for Patch Management of Control Systems

Energy Technology Data Exchange (ETDEWEB)

Steven Tom; Dale Christiansen; Dan Berrett

2008-12-01

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

Securing recommender systems against shilling attacks using social-based clustering

KAUST Repository

Zhang, Xiangliang

2013-07-01

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

A Data Management System Integrating Web-based Training and Randomized Trials: Requirements, Experiences and Recommendations.

Science.gov (United States)

Muroff, Jordana; Amodeo, Maryann; Larson, Mary Jo; Carey, Margaret; Loftin, Ralph D

2011-01-01

This article describes a data management system (DMS) developed to support a large-scale randomized study of an innovative web-course that was designed to improve substance abuse counselors' knowledge and skills in applying a substance abuse treatment method (i.e., cognitive behavioral therapy; CBT). The randomized trial compared the performance of web-course-trained participants (intervention group) and printed-manual-trained participants (comparison group) to determine the effectiveness of the web-course in teaching CBT skills. A single DMS was needed to support all aspects of the study: web-course delivery and management, as well as randomized trial management. The authors briefly reviewed several other systems that were described as built either to handle randomized trials or to deliver and evaluate web-based training. However it was clear that these systems fell short of meeting our needs for simultaneous, coordinated management of the web-course and the randomized trial. New England Research Institute's (NERI) proprietary Advanced Data Entry and Protocol Tracking (ADEPT) system was coupled with the web-programmed course and customized for our purposes. This article highlights the requirements for a DMS that operates at the intersection of web-based course management systems and randomized clinical trial systems, and the extent to which the coupled, customized ADEPT satisfied those requirements. Recommendations are included for institutions and individuals considering conducting randomized trials and web-based training programs, and seeking a DMS that can meet similar requirements.

Report on the Concept Review Committee recommendations for proof-of-principle alternate concept programs

International Nuclear Information System (INIS)

1979-03-01

The report is organized as follows: Section II contains a discussion of the meeting procedures used on October 16--18, 1978, and the rules employed for technical consultants and advisors to the CRC. Section III contains a discussion of the CRC recommendations and some of the factors taken into consideration by the Committee. Section IV briefly discusses where do we go from here in DOE's alternate concepts program

Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system

NARCIS (Netherlands)

Knijnenburg, B.P.; Willemsen, M.C.

2009-01-01

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

Overall Ventilation System Flow Network Calculation for Site Recommendation

International Nuclear Information System (INIS)

Steinhoff, Jeff J.

2001-01-01

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''

Challenges and opportunities of using recommender systems for personalized health education.

Science.gov (United States)

Fernandez-Luque, Luis; Karlsen, Randi; Vognild, Lars K

2009-01-01

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.

A Cooking Recipe Recommendation System with Visual Recognition of Food Ingredients

Directory of Open Access Journals (Sweden)

Keiji Yanai

2014-04-01

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.

Аdaptive clustering algorithm for recommender systems

OpenAIRE

Stekh, Yu.; Artsibasov, V.

2012-01-01

In this article adaptive clustering algorithm for recommender systems is developed. Розроблено адаптивний алгоритм кластеризації для рекомендаційних систем.

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Directory of Open Access Journals (Sweden)

Khalid Haruna

2017-12-01

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.

Recommender Systems for Technology Enhanced Learning: Research Trends & Applications

NARCIS (Netherlands)

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

2014-01-01

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

Nuclear proliferation and civilian nuclear power. Report of the Nonproliferation Alternative Systems Assessment Program. Volume IV. Commercial potential

Energy Technology Data Exchange (ETDEWEB)

1980-06-01

This volume of the Nonproliferation Alternative Systems Assessment Program (NASAP) report provides time and cost estimates for positioning new nuclear power systems for commercial deployment. The assessment also estimates the rates at which the new systems might penetrate the domestic market, assuming the continuing viability of the massive light-water reactor network that now exists worldwide. This assessment does not recommend specific, detailed program plans and budgets for individual systems; however, it is clear from this analysis that any of the systems investigated could be deployed if dictated by national interest.

Nuclear proliferation and civilian nuclear power. Report of the Nonproliferation Alternative Systems Assessment Program. Volume IV. Commercial potential

International Nuclear Information System (INIS)

1980-06-01

This volume of the Nonproliferation Alternative Systems Assessment Program (NASAP) report provides time and cost estimates for positioning new nuclear power systems for commercial deployment. The assessment also estimates the rates at which the new systems might penetrate the domestic market, assuming the continuing viability of the massive light-water reactor network that now exists worldwide. This assessment does not recommend specific, detailed program plans and budgets for individual systems; however, it is clear from this analysis that any of the systems investigated could be deployed if dictated by national interest

Towards Information Enrichment through Recommendation Sharing

Science.gov (United States)

Weng, Li-Tung; Xu, Yue; Li, Yuefeng; Nayak, Richi

Nowadays most existing recommender systems operate in a single organisational basis, i.e. a recommender system recommends items to customers of one organisation based on the organisation's datasets only. Very often the datasets of a single organisation do not have sufficient resources to be used to generate quality recommendations. Therefore, it would be beneficial if recommender systems of different organisations with similar nature can cooperate together to share their resources and recommendations. In this chapter, we present an Ecommerce-oriented Distributed Recommender System (EDRS) that consists of multiple recommender systems from different organisations. By sharing resources and recommendations with each other, these recommenders in the distributed recommendation system can provide better recommendation service to their users. As for most of the distributed systems, peer selection is often an important aspect. This chapter also presents a recommender selection technique for the proposed EDRS, and it selects and profiles recommenders based on their stability, average performance and selection frequency. Based on our experiments, it is shown that recommenders' recommendation quality can be effectively improved by adopting the proposed EDRS and the associated peer selection technique.

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

Science.gov (United States)

Wang, Zongjiang

2011-12-01

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

The effect of preference elicitation methods on the user experience of a recommender system

NARCIS (Netherlands)

Knijnenburg, B.P.; Willemsen, M.C.

2010-01-01

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

Sustainable Monitoring and Surveillance Systems to Improve HIV Programs: Review.

Science.gov (United States)

Low-Beer, Daniel; Mahy, Mary; Renaud, Francoise; Calleja, Txema

2018-04-24

HIV programs have provided a major impetus for investments in surveillance data, with 5-10% of HIV program budgets recommended to support data. However there are questions concerning the sustainability of these investments. The Sustainable Development Goals have consolidated health into one goal and communicable diseases into one target (Target 3.3). Sustainable Development Goals now introduce targets focused specifically on data (Targets 17.18 and 17.19). Data are seen as one of the three systemic issues (in Goal 17) for implementing Sustainable Development Goals, alongside policies and partnerships. This paper reviews the surveillance priorities in the context of the Sustainable Development Goals and highlights the shift from periodic measurement towards sustainable disaggregated, real-time, case, and patient data, which are used routinely to improve programs. Finally, the key directions in developing person-centered monitoring systems are assessed with country examples. The directions contribute to the Sustainable Development Goal focus on people-centered development applied to data. ©Daniel Low-Beer, Mary Mahy, Francoise Renaud, Txema Calleja. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 24.04.2018.

1974 review of the research program

International Nuclear Information System (INIS)

1975-01-01

The role of the Research Program in Controlled Thermonuclear Research, the activities that are contained within the Research Program, and summaries of the reports prepared by the study groups that analyzed the six activity areas that make up the Research Program are described. The recommendations by an ''Overview Panel'' are given. The recommendations are based on an analysis of the individual study group reports, consultations with CTR staff and field scientists, and on independent review of CTR program plans and needs. In some cases the recommendations of the Overview Panel are identical with study group recommendations and in other cases they are not. Some recommendations by the Overview Panel take into account factors and information that go beyond that available to the study groups. The five-year budget needed to accomplish the recommended Research Program is discussed. The Overview Panel chose to normalize its budget recommendations to the actual FY 1975 Research Program budget, reflecting the fact that this is already determined. The budgets for subsequent years are then based on this starting point. The complete reports prepared by the six study groups are given. Each report is based on an analysis of the needs as dictated by the Magnetic Confinement Systems and Development and Technology Program Plans. (U.S.)

Movie Recommendation System Based on Collaborative Filtering

OpenAIRE

Kejun, Wang

2013-01-01

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...

Key Findings and Recommendations for Technology Transfer at the ITS JPO

Science.gov (United States)

2011-03-18

This report provides key findings and recommendations for technology transfer at the Intelligent Transportation Systems Joint Program Office (ITS JPO) based upon an assessment of best practices in technology transfer in other industries, such as nati...

An Assessment of some Fertilizer Recommendations under Different Cropping Systems in a Humid Tropical Environment

Directory of Open Access Journals (Sweden)

Fondufe, EY.

2001-01-01

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.

A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems

Directory of Open Access Journals (Sweden)

javad nehriri

2017-12-01

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.

Report on RecSys 2016 Workshop on New Trends in Content-Based Recommender Systems

DEFF Research Database (Denmark)

Bogers, Toine; Koolen, Marijn; Musto, Cataldo

2017-01-01

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...

Modeling Temporal Bias of Uplift Events in Recommender Systems

KAUST Repository

Altaf, Basmah

2013-05-08

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

A Probability-Based Hybrid User Model for Recommendation System

Directory of Open Access Journals (Sweden)

Jia Hao

2016-01-01

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

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

Science.gov (United States)

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

2016-12-01

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

An effective trust-based recommendation method using a novel graph clustering algorithm

Science.gov (United States)

Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

2015-10-01

Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

Directory of Open Access Journals (Sweden)

Yan Guo

2018-01-01

Full Text Available A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers’ requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF network in order to determine the weights of recommendations, and an improved Dempster–Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business.

Recommendations for NEAMS Engagement with the NRC: Preliminary Report

International Nuclear Information System (INIS)

Bernholdt, David E.

2012-01-01

The vision of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to bring a new generation of analytic tools to the nuclear engineering community in order to facilitate students, faculty, industry and laboratory researchers in investigating advanced reactor and fuel cycle designs. Although primarily targeting at advance nuclear technologies, it is anticipated that these new capabilities will also become interesting and useful to the nuclear regulator Consequently, the NEAMS program needs to engage with the Nuclear Regulatory Commission as the software is being developed to ensure that they are familiar with and ready to respond to this novel approach when the need arises. Through discussions between key NEAMS and NRC staff members, we tentatively recommend annual briefings to the Division of Systems Analysis in the NRC's Office of Nuclear Regulatory Research. However the NEAC subcommittee review of the NEAMS program may yield recommendations that would need to be considered before finalizing this plan.

Recommendations for NEAMS Engagement with the NRC: Preliminary Report

Energy Technology Data Exchange (ETDEWEB)

Bernholdt, David E [ORNL

2012-06-01

The vision of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to bring a new generation of analytic tools to the nuclear engineering community in order to facilitate students, faculty, industry and laboratory researchers in investigating advanced reactor and fuel cycle designs. Although primarily targeting at advance nuclear technologies, it is anticipated that these new capabilities will also become interesting and useful to the nuclear regulator Consequently, the NEAMS program needs to engage with the Nuclear Regulatory Commission as the software is being developed to ensure that they are familiar with and ready to respond to this novel approach when the need arises. Through discussions between key NEAMS and NRC staff members, we tentatively recommend annual briefings to the Division of Systems Analysis in the NRC's Office of Nuclear Regulatory Research. However the NEAC subcommittee review of the NEAMS program may yield recommendations that would need to be considered before finalizing this plan.

Redundancies in Data and their Effect on the Evaluation of Recommendation Systems

DEFF Research Database (Denmark)

Basaran, Daniel; Ntoutsi, Eirini; Zimek, Arthur

2017-01-01

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...

Recommender system based on scarce information mining.

Science.gov (United States)

Lu, Wei; Chung, Fu-Lai; Lai, Kunfeng; Zhang, Liang

2017-09-01

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.

Program management system manual

International Nuclear Information System (INIS)

1989-08-01

OCRWM has developed a program management system (PMS) to assist in organizing, planning, directing and controlling the Civilian Radioactive Waste Management Program. A well defined management system is necessary because: (1) the Program is a complex technical undertaking with a large number of participants, (2) the disposal and storage facilities to be developed by the Program must be licensed by the Nuclear Regulatory Commission (NRC) and hence are subject to rigorous quality assurance (QA) requirements, (3) the legislation mandating the Program creates a dichotomy between demanding schedules of performance and a requirement for close and continuous consultation and cooperation with external entities, (4) the various elements of the Program must be managed as parts of an integrated waste management system, (5) the Program has an estimated total system life cycle cost of over $30 billion, and (6) the Program has a unique fiduciary responsibility to the owners and generators of the nuclear waste for controlling costs and minimizing the user fees paid into the Nuclear Waste Fund. This PMS Manual is designed and structured to facilitate strong, effective Program management by providing policies and requirements for organizing, planning, directing and controlling the major Program functions

Adaptation of New Colombian Food-based Complementary Feeding Recommendations Using Linear Programming.

Science.gov (United States)

Tharrey, Marion; Olaya, Gilma A; Fewtrell, Mary; Ferguson, Elaine

2017-12-01

The aim of the study was to use linear programming (LP) analyses to adapt New Complementary Feeding Guidelines (NCFg) designed for infants aged 6 to 12 months living in poor socioeconomic circumstances in Bogota to ensure dietary adequacy for young children aged 12 to 23 months. A secondary data analysis was performed using dietary and anthropometric data collected from 12-month-old infants (n = 72) participating in a randomized controlled trial. LP analyses were performed to identify nutrients whose requirements were difficult to achieve using local foods as consumed; and to test and compare the NCFg and alternative food-based recommendations (FBRs) on the basis of dietary adequacy, for 11 micronutrients, at the population level. Thiamine recommended nutrient intakes for these young children could not be achieved given local foods as consumed. NCFg focusing only on meat, fruits, vegetables, and breast milk ensured dietary adequacy at the population level for only 4 micronutrients, increasing to 8 of 11 modelled micronutrients when the FBRs promoted legumes, dairy, vitamin A-rich vegetables, and chicken giblets. None of the FBRs tested ensured population-level dietary adequacy for thiamine, niacin, and iron unless a fortified infant food was recommended. The present study demonstrated the value of using LP to adapt NCFg for a different age group than the one for which they were designed. Our analyses suggest that to ensure dietary adequacy for 12- to 23-month olds these adaptations should include legumes, dairy products, vitamin A-rich vegetables, organ meat, and a fortified food.

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