Regret-based optimal recommendation sets in conversational recommender systems

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Regret-based optimal recommendation sets in conversational recommender systems

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ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents

New York, New York, USA

SESSION: Applications table of contents

Pages 101-108

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Paolo Viappiani	University of Toronto, Toronto, ON, Canada
Craig Boutilier	University of Toronto, Toronto, ON, Canada

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

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ABSTRACT

Current conversational recommender systems are unable to offer guarantees on the quality of their recommendations due to a lack of principled user utility models. We develop an approach to recommender systems that incorporates an explicit utility model into the recommendation process in a decision-theoretically sound fashion. The system maintains explicit constraints on user utility based on preferences revealed by the user's actions. We investigate a new decision criterion, setwise minimax regret (SMR), for constructing optimal recommendation sets: we develop algorithms for computing SMR, and prove that SMR determines choice sets for queries that are myopically optimal. This provides a natural basis for generating compound critiques in conversational recommender systems. Our simulation results suggest that this utility-theoretically sound approach to user modeling allows much more effective navigation of a product space than traditional approaches based on, for example, heuristic utility models and product similarity measures.

REFERENCES

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