The Adaptive Web pp 291-324

Collaborative Filtering Recommender Systems

  • J. Ben Schafer
  • Dan Frankowski
  • Jon Herlocker
  • Shilad Sen
Conference paper

DOI: 10.1007/978-3-540-72079-9_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4321)
Cite this paper as:
Schafer J.B., Frankowski D., Herlocker J., Sen S. (2007) Collaborative Filtering Recommender Systems. In: Brusilovsky P., Kobsa A., Nejdl W. (eds) The Adaptive Web. Lecture Notes in Computer Science, vol 4321. Springer, Berlin, Heidelberg

Abstract

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • J. Ben Schafer
    • 1
  • Dan Frankowski
    • 2
  • Jon Herlocker
    • 3
  • Shilad Sen
    • 2
  1. 1.Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614-0507 
  2. 2.Department of Computer Science, University of Minnesota, 4-192 EE/CS Building, 200 Union St. SE, Minneapolis, MN 55455 
  3. 3.School of Electrical Engineering and Computer Science, Oregon State University, 102 Dearborn Hall, Corvallis, OR 97331 

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