Personalized Conversational Case-Based
Recommendation

Mehmet H. Goker1 and Cynthia A. Thompson2

1DaimlerChrysler Research & Technology
1510 Page Mill Road, Palo Alto, CA 94304
mehmet.goeker@daimlerchrysler.com
2Center for the Study of Language and Information
Stanford University, Stanford, CA 94305-4115
cthomp@csli.stanford.edu



Abstract: In this paper, we describe the Adaptive Place Advisor, a user
adaptive, conversational recommendation system designed to help users
decide on a destination, specifically a restaurant. We view the selection
of destinations as an interactive, conversational process, with the
advisory system inquiring about desired item characteristics and the
human responding. The user model, which contains preferences
regarding items, attributes, values, value combinations, and
diversification, is also acquired during the conversation. The system
enhances the users requirements with the user model and retrieves
suitable items from a case-base. If the number of items found by the
system is unsuitable (too high, too low) the next attribute to be
constrained or relaxed is selected based on the information gain
associated with the attributes. We also describe the current status of the
system and future work.
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