Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets
published: March 25, 2011, recorded: December 2010, views: 3131
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
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 asked 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 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.
See Also:
Download slides: nips2010_viappiani_obr_01.pdf (355.6 KB)
Download article: nips2010_0444.pdf (186.7 KB)
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: