Learning Bayesian Networks With Hidden Variables for User Modeling

By Barbara Großmann-Hutter, Anthony Jameson, and Frank Wittig (1999)

Proceedings of the IJCAI 99 Workshop “Learning About Users”, Stockholm, S. 29–34.

Abstract

We present issues and initial results of our research into methods for learning Bayesian networks for user modeling on the basis of empirical data, focusing on issues that are especially important in the context of user modeling. These issues include the treatment of theoretically interpretable hidden variables, ways of learning partial networks and combining them into a single network, ways of taking into account the special properties of datasets acquired through psychological experiments, and ways of increasing the efficiency and effectiveness of the learning algorithms.

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BibTeX entry

@inproceedings{Grossmann-HutterJW99,
  year = {1999},
  author = {{Gro{\ss}mann-Hutter}, Barbara and
            {Jameson}, Anthony and
            {Wittig}, Frank},
  title = {Learning {B}ayesian Networks With Hidden Variables for User Modeling},
  booktitle = {Proceedings of the {IJCAI~99} {W}orkshop ``{L}earning {A}bout
  {U}sers'},
  address = {Stockholm},
  pages = {29--34}}