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Learning and Revising User Profiles: The Identification of Interesting Web Sites

Abstract

We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy.

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Pazzani, M., Billsus, D. Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27, 313–331 (1997). https://doi.org/10.1023/A:1007369909943

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  • DOI: https://doi.org/10.1023/A:1007369909943

  • Information filtering
  • intelligent agents
  • multistrategy learning
  • World Wide Web
  • user profiles