bookCambridge University Press eBooksSep 30, 2010Closed access

Recommender Systems

TU Dortmund University · University of Klagenfurt · +1 more institution

Indexed incrossref

Abstract

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical…

Citation impact

841
total citations
FWCI
24.51
Percentile
100%
References
0
Citations per year

Authors

4

Topics & keywords

Keywords
  • Recommender system
  • Information overload
  • Computer science
  • Variety (cybernetics)
  • Collaborative filtering
  • Field (mathematics)
  • Quality (philosophy)
  • World Wide Web
UN Sustainable Development Goals
  • Industry, innovation and infrastructure
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