Preference elicitation for interface optimization

  • Authors:
  • Krzysztof Gajos;Daniel S. Weld

  • Affiliations:
  • University of Washington, Seattle, WA;University of Washington, Seattle, WA

  • Venue:
  • Proceedings of the 18th annual ACM symposium on User interface software and technology
  • Year:
  • 2005

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Abstract

Decision-theoretic optimization is becoming a popular tool in the user interface community, but creating accurate cost (or utility) functions has become a bottleneck --- in most cases the numerous parameters of these functions are chosen manually, which is a tedious and error-prone process. This paper describes ARNAULD, a general interactive tool for eliciting user preferences concerning concrete outcomes and using this feedback to automatically learn a factored cost function. We empirically evaluate our machine learning algorithm and two automatic query generation approaches and report on an informal user study.