A Framework for Collaborative, Content-Based and Demographic Filtering

  • Authors:
  • Michael J. Pazzani

  • Affiliations:
  • Department of Information and Computer Science, University of California, 444 Computer Science Building, Irvine, CA 92697, USA (E-mail: pazzani@ics.uci.edu)

  • Venue:
  • Artificial Intelligence Review - Special issue on data mining on the Internet
  • Year:
  • 1999

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Abstract

We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.