Chapter

The Adaptive Web

Volume 4321 of the series Lecture Notes in Computer Science pp 291-324

Collaborative Filtering Recommender Systems

  • J. Ben SchaferAffiliated withDepartment of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614-0507
  • , Dan FrankowskiAffiliated withDepartment of Computer Science, University of Minnesota, 4-192 EE/CS Building, 200 Union St. SE, Minneapolis, MN 55455
  • , Jon HerlockerAffiliated withSchool of Electrical Engineering and Computer Science, Oregon State University, 102 Dearborn Hall, Corvallis, OR 97331
  • , Shilad SenAffiliated withDepartment of Computer Science, University of Minnesota, 4-192 EE/CS Building, 200 Union St. SE, Minneapolis, MN 55455

* Final gross prices may vary according to local VAT.

Get Access

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.