Regret-based optimal recommendation sets in conversational recommender systems

Authors:
Paolo Viappiani;Craig Boutilier
Affiliations:
University of Toronto, Toronto, ON, Canada;University of Toronto, Toronto, ON, Canada
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

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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.