Cased-Based Preference Elicitation (Preliminary Report)

Vu Ha, Tri Le, and Peter Haddawy

While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decision-theoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment directly elicited preferences with some appropriate default information. In this paper we propose a case-based approach to alleviating the preference elicitation bottleneck. Assuming the existence of a population of users from whom we have elicited complete or incomplete preference models, we propose eliciting the preference model of a new user interactively and incrementally, using the closest existing preference models as potential defaults. A notion of closeness demands a measure of distance among preference orders, which is formally defined and investigated.


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