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Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets
Paolo Viappiani · Craig E Boutilier

Wed Dec 08 09:40 AM -- 09:45 AM (PST) @ Regency Ballroom

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 \emph{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 \emph{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 follow mixed multinomial logit noise models) and provide worst-case guarantees.
Finally we present a local search technique that works well
with large outcome spaces.

Author Information

Paolo Viappiani (CNRS)
Craig E Boutilier (University of Toronto)

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