Machine Learning for User Modeling

  • Geoffrey I. Webb
  • Michael J. Pazzani
  • Daniel Billsus


At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.

user modeling machine learning concept drift computational complexity World Wide Web information agents 


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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Geoffrey I. Webb
    • 1
  • Michael J. Pazzani
    • 2
  • Daniel Billsus
    • 2
  1. 1.School of Computing and MathematicsDeakin UniversityGeelongAustralia
  2. 2.Department of Information and Computer ScienceUniversity of California, IrvineIrvineU.S.A

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