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Seminar: Dr. Paolo Viappiani, "Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets," Nov. 22, 2010 at 12:30 pm, CL 417 (Expired)

Department of Computer Science



SPEAKER:     Dr. Paolo Viappiani

DATE:             November 22, 2010


TIME:              12:30 pm


PLACE:           CL 417


TITLE:             Optimal Bayesian Recommendation Sets and Myopically Optimal

Choice Query Sets




Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOI-optimization is usually computationally prohibitive.  In this paper, we examine EVOI optimization using "choice queries", queries in which a user is ask to select her most preferred product from a set. We show that, under very general assumptions, the optimal choice query w.r.t. EVOI coincides with the "optimal recommendation set", that is, a set maximizing expected utility of the user selection. Since recommendation set optimization is a simpler, submodular problem, this can greatly reduce the complexity of both exact and approximate (greedy) computation of optimal choice queries.  We also examine the case where user responses to choice queries are error-prone (using both constant and mixed multinomial logit noise models) and provide worst-case guarantees. Finally we present a local search technique for query optimization that works extremely well with large outcome spaces.  These results are analogous to previous findings in regret-based elicitation, which we published last year, and we provide an overview of these as well.


Joint work with Craig Boutilier.




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