Regret-based optimal recommendation sets in conversational recommender systems

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Regret-based optimal recommendation sets in conversational recommender systems
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 u...
Paolo Viappiani, Craig Boutilier
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Authors Paolo Viappiani, Craig Boutilier
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