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Preference elicitation with subjective features

Published: 23 October 2009 Publication History

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.

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cover image ACM Conferences
RecSys '09: Proceedings of the third ACM conference on Recommender systems
October 2009
442 pages
ISBN:9781605584355
DOI:10.1145/1639714
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 October 2009

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Author Tags

  1. concept learning
  2. minimax regret
  3. preference elicitation
  4. recommender systems
  5. version space

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RecSys '09
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RecSys '09: Third ACM Conference on Recommender Systems
October 23 - 25, 2009
New York, New York, USA

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