Skip to main content

Recommender Systems Handbook

  • Book
  • © 2015

Overview

  • Includes major updates as well as 20 new chapters
  • Presents detailed case studies
  • Shares tips and insights from renowned experts in the field

This is a preview of subscription content, log in via an institution to check access.

Access this book

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

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.

Similar content being viewed by others

Keywords

Table of contents (28 chapters)

  1. Recommendation Techniques

  2. Recommender Systems Evaluation

  3. Recommendation Techniques

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

  • 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

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

  • Book Title: Recommender Systems Handbook

  • Editors: Francesco Ricci, Lior Rokach, Bracha Shapira

  • DOI: https://doi.org/10.1007/978-1-4899-7637-6

  • Publisher: Springer New York, NY

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Science+Business Media New York 2015

  • Softcover ISBN: 978-1-4899-7780-9Published: 23 August 2016

  • eBook ISBN: 978-1-4899-7637-6Published: 17 November 2015

  • Edition Number: 2

  • Number of Pages: XVII, 1003

  • Topics: Information Storage and Retrieval, Artificial Intelligence

Publish with us