Toward Case­Based Preference Elicitation: Similarity Measures on Preference Structures

Vu Ha, Peter Haddawy Decision Systems and Artificial Intelligence Lab Dept. of EE&CS University of Wisconsin­Milwaukee Milwaukee, WI 53201

Abstract

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 sys­ tems this overhead precludes the use of for­ mal decision­theoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment di­ rectly elicited preferences with some appro­ priate default information. In this paper we propose a case­based approach to alleviat­ ing the preference elicitation bottleneck. As­ suming the existence of a population of users from whom we have elicited complete or in­ complete preference structures, we propose eliciting the preferences of a new user inter­ actively and incrementally, using the closest existing preference structures as potential de­ faults. Since a notion of closeness demands a measure of distance among preference struc­ tures, this paper takes the first step of study­ ing various distance measures over fully and partially specified preference structures. We explore the use of Euclidean distance, Spear­ man's footrule, and define a new measure, the probabilistic distance. We provide computa­ tional techniques for all three measures.