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Recommender Systems Handbook
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Recommender Systems Handbook
1060Paperback(3rd ed. 2022)
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Overview
The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation.
This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.
Product Details
ISBN-13: | 9781071621998 |
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Publisher: | Springer US |
Publication date: | 04/23/2023 |
Edition description: | 3rd ed. 2022 |
Pages: | 1060 |
Product dimensions: | 6.10(w) x 9.25(h) x (d) |
About the Author
Lior Rokach is a data scientist and a professor of Software and Information Systems Engineering (SISE) atBen-Gurion University of the Negev (BGU). His research interests lie in the design, development, and analysis of Machine Learning and Data Mining algorithms and their applications in Recommender Systems, Cyber Security and Medical Informatics. Rokach has co-founded four AI companies and has been awarded 22 patents for his inventions in AI and information technology. Prof. Rokach is the author of over 300 peer-reviewed papers in leading journals and conference proceedings. He is also the author of six books and the editor of three books.
Bracha Shapira is a professor of Software and Information Systems Engineering (SISE) at Ben-Gurion University of the Negev (BGU), and an active data scientist. She has been leading research projects at the Deutsche Telekom lab at Ben-Gurion University, and is a member of the Cyber at BGU center, where she applies machine learning methods to many domains, including recommender systems, cyber security and medical informatics. She has published more than 200 papers in leading journals and conferences and has been awarded more than 20 patents for her inventions.
Table of Contents
Preface.- Introduction.- Part 1: General Recommendation Techniques.- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers).- Advances in Collaborative Filtering (Koren).- Item Recommendation from Implicit Feedback (Rendle).- Deep Learning for Recommender Systems (Zhang).- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman).- Semantics and Content-based Recommendations (Musto).- Part 2: Special Recommendation Techniques.- Session-based Recommender Systems (lannoch)..- Adversarial Recommender Systems: Attack,Defense, and Advances (Di Nola).- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff).- People-to-People Reciprocal Recommenders (Koprinska).- Natural Language Processing for Recommender Systems (Sar-Shalom).- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi).- Part 3: Value and Impact of Recommender Systems.- Value and Impact of Recommender Systems (Zanker).- Evaluating Recommender Systems (Shani).- Novelty and Diversity in Recommender Systems (Castells).- Multistakeholder Recommender Systems (Burke).- Fairness in Recommender Systems (Ekstrand).- Part 4: Human Computer Interaction.- Beyond Explaining Single Item Recommendations (Tintarev).- Personality and Recommender Systems (Tkalčič).- Individual and Group Decision Making and Recommender Systems (Jameson).- Part 5: Recommender Systems Applications .- Social Recommender Systems (Guy).- Food Recommender Systems (Trattner).- Music Recommendation Systems: Techniques, Use Cases, and Challenges (Schedl).- Multimedia Recommender Systems: Algorithms and Challenges (Deldjoo).- Fashion Recommender Systems (Dokoohaki).