Recommender Systems Handbook

1. Front Matter

   Pages i-xvii

   PDF

2. Recommender Systems: Introduction and Challenges

   • Francesco Ricci, Lior Rokach, Bracha Shapira

   Pages 1-34

3. Recommendation Techniques

   1. Front Matter

      Pages 35-35

      PDF

   2. A Comprehensive Survey of Neighborhood-Based Recommendation Methods

      • Xia Ning, Christian Desrosiers, George Karypis

      Pages 37-76

   3. Advances in Collaborative Filtering

      • Yehuda Koren, Robert Bell

      Pages 77-118

   4. Semantics-Aware Content-Based Recommender Systems

      • Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Giovanni Semeraro

      Pages 119-159

   5. Constraint-Based Recommender Systems

      • Alexander Felfernig, Gerhard Friedrich, Dietmar Jannach, Markus Zanker

      Pages 161-190

   6. Context-Aware Recommender Systems

      • Gediminas Adomavicius, Alexander Tuzhilin

      Pages 191-226

   7. Data Mining Methods for Recommender Systems

      • Xavier Amatriain, Josep M. Pujol

      Pages 227-262

4. Recommender Systems Evaluation

   1. Front Matter

      Pages 263-263

      PDF

   2. Evaluating Recommender Systems

      • Asela Gunawardana, Guy Shani

      Pages 265-308

   3. Evaluating Recommender Systems with User Experiments

      • Bart P. Knijnenburg, Martijn C. Willemsen

      Pages 309-352

   4. Explaining Recommendations: Design and Evaluation

      • Nava Tintarev, Judith Masthoff

      Pages 353-382

5. Recommendation Techniques

   1. Front Matter

      Pages 383-383

      PDF

   2. Recommender Systems in Industry: A Netflix Case Study

      • Xavier Amatriain, Justin Basilico

      Pages 385-419

   3. Panorama of Recommender Systems to Support Learning

      • Hendrik Drachsler, Katrien Verbert, Olga C. Santos, Nikos Manouselis

      Pages 421-451

   4. Music Recommender Systems

      • Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, Marius Kaminskas

      Pages 453-492

   5. The Anatomy of Mobile Location-Based Recommender Systems

      • Neal Lathia

      Pages 493-510

   6. Social Recommender Systems

      • Ido Guy

      Pages 511-543

   7. People-to-People Reciprocal Recommenders

      • Irena Koprinska, Kalina Yacef

      Pages 545-567

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.

Policies and ethics