Content-boosted collaborative filtering for improved recommendations

  • Authors:
  • Prem Melville;Raymod J. Mooney;Ramadass Nagarajan

  • Affiliations:
  • Department of Computer Sciences, University of Texas, Austin, TX;Department of Computer Sciences, University of Texas, Austin, TX;Department of Computer Sciences, University of Texas, Austin, TX

  • Venue:
  • Eighteenth national conference on Artificial intelligence
  • Year:
  • 2002

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Abstract

Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.