© 2015

Recommender Systems Handbook

  • Francesco Ricci
  • Lior Rokach
  • Bracha Shapira

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Francesco Ricci, Lior Rokach, Bracha Shapira
    Pages 1-34
  3. Recommendation Techniques

    1. Front Matter
      Pages 35-35
    2. Xia Ning, Christian Desrosiers, George Karypis
      Pages 37-76
    3. Yehuda Koren, Robert Bell
      Pages 77-118
    4. Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Giovanni Semeraro
      Pages 119-159
    5. Alexander Felfernig, Gerhard Friedrich, Dietmar Jannach, Markus Zanker
      Pages 161-190
    6. Gediminas Adomavicius, Alexander Tuzhilin
      Pages 191-226
    7. Xavier Amatriain, Josep M. Pujol
      Pages 227-262
  4. Recommender Systems Evaluation

    1. Front Matter
      Pages 263-263
    2. Asela Gunawardana, Guy Shani
      Pages 265-308
    3. Bart P. Knijnenburg, Martijn C. Willemsen
      Pages 309-352
    4. Nava Tintarev, Judith Masthoff
      Pages 353-382
  5. Recommendation Techniques

    1. Front Matter
      Pages 383-383
    2. Xavier Amatriain, Justin Basilico
      Pages 385-419
    3. Hendrik Drachsler, Katrien Verbert, Olga C. Santos, Nikos Manouselis
      Pages 421-451
    4. Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, Marius Kaminskas
      Pages 453-492
    5. Ido Guy
      Pages 511-543
    6. Irena Koprinska, Kalina Yacef
      Pages 545-567

About this book


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

Editors and affiliations

  • Francesco Ricci
    • 1
  • Lior Rokach
    • 2
  • Bracha Shapira
    • 3
  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzano - BozenItaly
  2. 2.Information Systems EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  3. 3.Ben-Gurion University of the NegevBeer-ShevaIsrael

About the editors

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.

Bibliographic information

Industry Sectors
IT & Software
Consumer Packaged Goods
Finance, Business & Banking


“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)