Overview
- Editors:
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Francesco Ricci
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Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy
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Lior Rokach
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Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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Bracha Shapira
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Ben-Gurion University of the Negev, Beer-Sheva, Israel
Includes major updates as well as 20 new chapters
Presents detailed case studies
Shares tips and insights from renowned experts in the field
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Table of contents (28 chapters)
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Front Matter
Pages i-xvii
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- Francesco Ricci, Lior Rokach, Bracha Shapira
Pages 1-34
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Recommendation Techniques
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- Xia Ning, Christian Desrosiers, George Karypis
Pages 37-76
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- Yehuda Koren, Robert Bell
Pages 77-118
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- Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Giovanni Semeraro
Pages 119-159
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- Alexander Felfernig, Gerhard Friedrich, Dietmar Jannach, Markus Zanker
Pages 161-190
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- Gediminas Adomavicius, Alexander Tuzhilin
Pages 191-226
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- Xavier Amatriain, Josep M. Pujol
Pages 227-262
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Recommender Systems Evaluation
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Front Matter
Pages 263-263
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- Asela Gunawardana, Guy Shani
Pages 265-308
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- Bart P. Knijnenburg, Martijn C. Willemsen
Pages 309-352
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- Nava Tintarev, Judith Masthoff
Pages 353-382
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Recommendation Techniques
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Front Matter
Pages 383-383
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- Xavier Amatriain, Justin Basilico
Pages 385-419
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- Hendrik Drachsler, Katrien Verbert, Olga C. Santos, Nikos Manouselis
Pages 421-451
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- Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, Marius Kaminskas
Pages 453-492
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- 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.
Reviews
“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)
Editors and Affiliations
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Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy
Francesco Ricci
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Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Lior Rokach
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Ben-Gurion University of the Negev, Beer-Sheva, Israel
Bracha Shapira
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.
