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Recommender Systems Handbook
Title
Recommender Systems Handbook [electronic resource] / edited by Francesco Ricci, Lior Rokach, Bracha Shapira.
ISBN
9781071621974
Edition
3rd ed. 2022.
Publication
New York, NY : Springer US : Imprint: Springer, 2022.
Physical Description
1 online resource (XI, 1061 p.) 129 illus., 105 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. 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. .
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
April 22, 2022
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).
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