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Leveraging Data About Users in General in the Learning of Individual User Models

By Anthony Jameson and Frank Wittig (2001)

In B. Nebel (Ed.), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 1185–1192). San Francisco: Morgan Kaufmann.

Abstract

Models of computer users that are learned on the basis of data can make use of two types of information: data about users in general and data about the current individual user. Focusing on user models that take the form of Bayesian networks, we compare four types of model that represent different ways of combining these two types of data. Models of the four types are applied to the data of an experiment, and they are evaluated according to theoretical, empirical, and practical criteria. One of the model types is a new variant of the aHugin method for adapting the probabilities of a Bayesian network while it is being used: Differential adaptation is a principled way of determining the speed with which each aspect of a network is adapted to an individual user.

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BibTeX entry

@incollection{JamesonW01,
  year = {2001},
  author = {{Jameson}, Anthony and
            {Wittig}, Frank},
  editor = {{Nebel}, Bernhard},
  title = {Leveraging Data About Users in General in the Learning of Individual User Models},
  booktitle = {{P}roceedings of the {S}eventeenth {I}nternational {J}oint {C}onference on {A}rtificial
    {I}ntelligence},
  address = {San Francisco},
  publisher = {Morgan Kaufmann},
  pages = {1185--1192}}