Recommender Systems Handbook

1. Recommendation Techniques

   1. Collaboration, Reputation and Recommender Systems in Social Web Search

      • Barry Smyth, Maurice Coyle, Peter Briggs, Kevin McNally, Michael P. O’Mahony

      Pages 569-608

2. Human Computer Interaction

   1. Front Matter

      Pages 609-609

      PDF

   2. Human Decision Making and Recommender Systems

      • Anthony Jameson, Martijn C. Willemsen, Alexander Felfernig, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro et al.

      Pages 611-648

   3. Privacy Aspects of Recommender Systems

      • Arik Friedman, Bart P. Knijnenburg, Kris Vanhecke, Luc Martens, Shlomo Berkovsky

      Pages 649-688

   4. Source Factors in Recommender System Credibility Evaluation

      • Kyung-Hyan Yoo, Ulrike Gretzel, Markus Zanker

      Pages 689-714

   5. Personality and Recommender Systems

      • Marko Tkalcic, Li Chen

      Pages 715-739

3. Advanced Topics

   1. Front Matter

      Pages 741-741

      PDF

   2. Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

      • Judith Masthoff

      Pages 743-776

   3. Aggregation Functions for Recommender Systems

      • Gleb Beliakov, Tomasa Calvo, Simon James

      Pages 777-808

   4. Active Learning in Recommender Systems

      • Neil Rubens, Mehdi Elahi, Masashi Sugiyama, Dain Kaplan

      Pages 809-846

   5. Multi-Criteria Recommender Systems

      • Gediminas Adomavicius, YoungOk Kwon

      Pages 847-880

   6. Novelty and Diversity in Recommender Systems

      • Pablo Castells, Neil J. Hurley, Saul Vargas

      Pages 881-918

   7. Cross-Domain Recommender Systems

      • Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, Paolo Cremonesi

      Pages 919-959

   8. Robust Collaborative Recommendation

      • Robin Burke, Michael P. O’Mahony, Neil J. Hurley

      Pages 961-995

4. Back Matter

   Pages 997-1003

   PDF

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.

• Collaborative filtering
• Collective intelligence
• Context-aware systems
• Data mining
• Data science
• Decision making
• Decision support systems
• Industrial systems
• Information retrieval
• Intelligent user interface
• Machine learning
• Mobile recommender systems
• Personalization
• Recommender systems
• Social networks
• Web media

“If you have time for just one book to get yourself up to speed with the latest and best in recommender systems, this is the book you want. … this is an excellent educational resource on the main techniques employed for making recommendations … . is definitely a book to read to get updated on the state of the art of recommender systems, and also to get a feel of the breadth of the research areas available in this area.” (Jun-Ping Ng, Computing Reviews, April, 2016)

• Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

  Francesco Ricci

• Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

  Lior Rokach

• Ben-Gurion University of the Negev, Beer-Sheva, Israel

  Bracha Shapira

Francesco Ricci is a professor of computer science at the Free University of Bozen-Bolzano, Italy. His current research interests include recommender systems, intelligent interfaces, mobile systems, machine learning, case-based reasoning, and the applications of ICT to health and tourism. He has published more than one hundred thirty of academic papers on these topics. He is the editor in chief of the Journal of Information Technology & Tourism and on the editorial board of User Modeling and User Adapted Interaction. Lior Rokach is a data scientist and an associate professor of information systems and software engineering at Ben-Gurion University of the Negev (BGU). Rokach established the machine learning laboratory in BGU which promotes innovative adaptations of machine learning and data mining methods to create the next generation of intelligent systems. Rokach is known for his contributions to the advancement of machine learning, recommender systems and cyber security. Bracha Shapira is an associate professor and the head of the information systems and engineering Department at Ben-Gurion University of the Negev (BGU). She leads large scale research projects at the Telekom Innovation Laboratories at BGU in the area of data analytics, recommender systems and personalization that delivers innovative technologies to address challenges in these fields. Shapira is known for her contribution in integrating social network, context awareness and privacy consideration to recommender systems.

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