Preference elicitation with subjective features

  • Authors:

  • Craig Boutilier;Kevin Regan;Paolo Viappiani

  • Affiliations:

  • University of Toronto, Toronto, ON, Canada;University of Toronto, Toronto, ON, Canada;University of Toronto, Toronto, ON, Canada

  • Venue:
  • Proceedings of the third ACM conference on Recommender systems
  • Year:
  • 2009

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Abstract

Utility or preference elicitation is a critical component in many recommender and decision support systems. However, most frameworks for elicitation assume a predefined set of features (e.g., as derived from catalog descriptions) over which user preferences are expressed. Just as user preferences vary considerably, so too can the features over which they are most comfortable expressing these preferences. In this work, we consider preference elicitation in the presence of subjective or user-defined features. We treat the problem of learning a user's feature definition as one of concept learning, but whose goal is to learn only enough about the concept definition to enable a good decision to be made. This is complicated by the fact that user preferences are unknown. We describe computational procedures for identifying optimal alternatives w.r.t minimax regret in the presence of both utility and concept uncertainty; and develop several heuristic query strategies that focus on reduction of relevant concept and utility uncertainty. Computational experiments verify the efficacy of these strategies.