NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
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CONTENTS NOTE
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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfu.
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Chapter 18 Communities, Collaboration, and Recommender Systems in PersonalizedWeb SearchChapter 19 Social Tagging Recommender Systems; Chapter 20 Trust and Recommendations; Chapter 21 Group Recommender Systems:Combining Individual Models; Part V Advanced Algorithms; Chapter 22 Aggregation of Preferences in Recommender Systems; Chapter 23 Active Learning in Recommender Systems; Chapter 24 Multi-Criteria Recommender Systems; Chapter 25 Robust Collaborative Recommendation; Index;
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Part III Interacting with Recommender SystemsChapter 13 On the Evolution of Critiquing Recommenders; Chapter 14 Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations; Chapter 15 Designing and Evaluating Explanations for Recommender Systems; Chapter 16 Usability Guidelines for Product Recommenders Based on Example Critiquing Research; Chapter 17 Map Based Visualization of Product Catalogs; Part IV Recommender Systems and Communities.
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Chapter 4 A Comprehensive Survey of Neighborhood-based Recommendation MethodsChapter 5 Advances in Collaborative Filtering; Chapter 6 Developing Constraint-based Recommenders; Chapter 7 Context-Aware Recommender Systems; Part II Applications and Evaluation of RSs; Chapter 8 Evaluating Recommendation Systems; Chapter 9 A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment; Chapter 10 How to Get the Recommender Out of the Lab?; Chapter 11 Matching Recommendation Technologies andDomains; Chapter 12 Recommender Systems in Technology Enhanced Learning.
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Preface; Contents; List of Contributors; Chapter 1 Introduction to Recommender Systems Handbook; 1.1 Introduction; 1.2 Recommender Systems Function; 1.3 Data and Knowledge Sources; 1.4 Recommendation Techniques; 1.5 Application and Evaluation; 1.6 Recommender Systems and Human Computer Interaction; 1.7 Recommender Systems as a Multi-Disciplinary Field; 1.8 Emerging Topics and Challenges; References; Part I Basic Techniques; Chapter 2 Data Mining Methods for Recommender Systems; Chapter 3 Content-based Recommender Systems: State of the Art and Trends.