Towards Case-Based Preference Elicitation: Similarity Measures on
  Preference Structures
  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 structures,
we propose eliciting the preferences of a new user interactively and
incrementally, using the closest existing preference structures as potential
defaults. Since a notion of closeness demands a measure of distance among
preference structures, this paper takes the first step of studying various
distance measures over fully and partially specified preference structures. We
explore the use of Euclidean distance, Spearmans footrule, and define a new
measure, the probabilistic distance. We provide computational techniques for
all three measures.
