Machine Learning for User Modeling

  • Authors:
  • Geoffrey I. Webb;Michael J. Pazzani;Daniel Billsus

  • Affiliations:
  • School of Computing and Mathematics, Deakin University, Geelong, Victoria 3217, Australia;Department of Information and Computer Science, University of California, Irvine, Irvine, California 92697, U.S.A.;Department of Information and Computer Science, University of California, Irvine, Irvine, California 92697, U.S.A.

  • Venue:
  • User Modeling and User-Adapted Interaction
  • Year:
  • 2001

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Abstract

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.