Recommender Systems Handbook

✍ Scribed by Francesco Ricci, Lior Rokach, Bracha Shapira (eds.)

• Publisher: Springer US
• Year: 2015
• Tongue: English
• Leaves: 1008
• Edition: 2
• Category: Library

✦ Synopsis

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

✦ Table of Contents

Front Matter....Pages i-xvii
Recommender Systems: Introduction and Challenges....Pages 1-34
Front Matter....Pages 35-35
A Comprehensive Survey of Neighborhood-Based Recommendation Methods....Pages 37-76
Advances in Collaborative Filtering....Pages 77-118
Semantics-Aware Content-Based Recommender Systems....Pages 119-159
Constraint-Based Recommender Systems....Pages 161-190
Context-Aware Recommender Systems....Pages 191-226
Data Mining Methods for Recommender Systems....Pages 227-262
Front Matter....Pages 263-263
Evaluating Recommender Systems....Pages 265-308
Evaluating Recommender Systems with User Experiments....Pages 309-352
Explaining Recommendations: Design and Evaluation....Pages 353-382
Front Matter....Pages 383-383
Recommender Systems in Industry: A Netflix Case Study....Pages 385-419
Panorama of Recommender Systems to Support Learning....Pages 421-451
Music Recommender Systems....Pages 453-492
The Anatomy of Mobile Location-Based Recommender Systems....Pages 493-510
Social Recommender Systems....Pages 511-543
People-to-People Reciprocal Recommenders....Pages 545-567
Collaboration, Reputation and Recommender Systems in Social Web Search....Pages 569-608
Front Matter....Pages 609-609
Human Decision Making and Recommender Systems....Pages 611-648
Privacy Aspects of Recommender Systems....Pages 649-688
Source Factors in Recommender System Credibility Evaluation....Pages 689-714
Front Matter....Pages 609-609
Personality and Recommender Systems....Pages 715-739
Front Matter....Pages 741-741
Group Recommender Systems: Aggregation, Satisfaction and Group Attributes....Pages 743-776
Aggregation Functions for Recommender Systems....Pages 777-808
Active Learning in Recommender Systems....Pages 809-846
Multi-Criteria Recommender Systems....Pages 847-880
Novelty and Diversity in Recommender Systems....Pages 881-918
Cross-Domain Recommender Systems....Pages 919-959
Robust Collaborative Recommendation....Pages 961-995
Back Matter....Pages 997-1003

✦ Subjects

Information Storage and Retrieval; Artificial Intelligence (incl. Robotics)