The Adaptive Web pp 325-341

Content-Based Recommendation Systems

  • Michael J. Pazzani
  • Daniel Billsus
Conference paper

DOI: 10.1007/978-3-540-72079-9_10

Volume 4321 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Pazzani M.J., Billsus D. (2007) Content-Based Recommendation Systems. In: Brusilovsky P., Kobsa A., Nejdl W. (eds) The Adaptive Web. Lecture Notes in Computer Science, vol 4321. Springer, Berlin, Heidelberg

Abstract

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Michael J. Pazzani
    • 1
  • Daniel Billsus
    • 2
  1. 1.Rutgers University, ASBIII, 3 Rutgers Plaza, New Brunswick, NJ 08901 
  2. 2.FX Palo Alto Laboratory, Inc., 3400 Hillview Ave, Bldg. 4, Palo Alto, CA 94304