Article

Optimal Bayesian recommendation sets and myopically optimal choice query sets

Authors:

Paolo Viappiani,

Craig BoutilierAuthors Info & Claims

NIPS'10: Proceedings of the 24th International Conference on Neural Information Processing Systems - Volume 2

Pages 2352 - 2360

Published: 06 December 2010 Publication History

Abstract

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 the 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.

References

[1]

Ali Abbas. Entropy methods for adaptive utility elicitation. IEEE Transactions on Systems, Science and Cybernetics, 34(2):169-178, 2004.

Digital Library

[2]

Craig Boutilier. A POMDP formulation of preference elicitation problems. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp.239-246, Edmonton, 2002.

Digital Library

[3]

Craig Boutilier, Relu Patrascu, Pascal Poupart, and Dale Schuurmans. Constraint-based optimization and utility elicitation using the minimax decision criterion. Artifical Intelligence, 170(8-9):686-713, 2006.

Digital Library

[4]

Craig Boutilier, Richard S. Zemel, and Benjamin Marlin. Active collaborative filtering. In Proc. 19th Conference on Uncertainty in Artificial Intelligence (UAI-03), pp.98-106, Acapulco, 2003.

Digital Library

[5]

Darius Braziunas and Craig Boutilier. Minimax regret-based elicitation of generalized additive utilities. In Proc. 23rd Conference on Uncertainty in Artificial Intelligence (UAI-07), pp.25-32, Vancouver, 2007.

[6]

U. Chajewska and D. Koller. Utilities as random variables: Density estimation and structure discovery. In Proc. 16th Conference on Uncertainty in Artificial Intelligence (UAI-00), pp.63-71, Stanford, 2000.

Digital Library

[7]

U. Chajewska, D. Koller, and R. Parr. Making rational decisions using adaptive utility elicitation. In Proc. 17th National Conference on Artificial Intelligence (AAAI-00), pp.363-369, Austin, TX, 2000.

Digital Library

[8]

Peter C. Fishburn. Interdependence and additivity in multivariate, unidimensional expected utility theory. International Economic Review, 8:335-342, 1967.

[9]

L. A. Wolsey G. L. Nemhauser and M. L. Fisher. An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 14(1):265-294, December 1978.

Digital Library

[10]

Krzysztof Gajos and Daniel S. Weld. Preference elicitation for interface optimization. In Patrick Baudisch, Mary Czerwinski, and Dan R. Olsen, editors, UIST, pp.173-182. ACM, 2005.

Digital Library

[11]

Shengbo Guo and Scott Sanner. Real-time multiattribute bayesian preference elicitation with pairwise comparison queries. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS-10), Sardinia, Italy, 2010.

[12]

V. S. Iyengar, J. Lee, and M. Campbell. Q-Eval: Evaluating multiple attribute items using queries. In Proceedings of the Third ACM Conference on Electronic Commerce, pp.144-153, Tampa, FL, 2001.

Digital Library

[13]

Ralph L. Keeney and Howard Raiffa. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York, 1976.

[14]

J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3):77-87, 1997.

Digital Library

[15]

Jordan J. Louviere, David A. Hensher, and Joffre D. Swait. Stated Choice Methods: Analysis and Application. Cambridge University Press, Cambridge, 2000.

Digital Library

[16]

Christopher G. Lucas, Thomas L. Griffiths, Fei Xu, and Christine Fawcett. A rational model of preference learning and choice prediction by children. In Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2008, pp.985-992, 2008.

Digital Library

[17]

Robert D. Luce. Individual choice behavior: a theoretical analysis. Wiley, New York, 1959.

[18]

Radford M. Neal. Slice sampling. The Annals of Statistics, 31(3):705-70, 2003.

[19]

A. Ng and S. Russell. Algorithms for inverse reinforcement learning. In Proc. 17th International Conference on Machine Learning (ICML-00), pp.663-670, Stanford, CA, 2000.

Digital Library

[20]

Robert Price and Paul R. Messinger. Optimal recommendation sets: Covering uncertainty over user preferences. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI'05), pp.541-548, 2005.

Digital Library

[21]

James Reilly, Kevin McCarthy, Lorraine McGinty, and Barry Smyth. Incremental critiquing. Knowledge-Based Systems, 18(4-5):143-151, 2005.

Digital Library

[22]

Olivier Toubia, John Hauser, and Duncan Simester. Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41:116-131, 2004.

[23]

Paolo Viappiani and Craig Boutilier. Regret-based optimal recommendation sets in conversational recommender systems. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys09), pp.101-108, New York, 2009.

Digital Library

[24]

Paolo Viappiani, Boi Faltings, and Pearl Pu. Preference-based search using example-critiquing with suggestions. Journal of Artificial Intelligence Research, 27:465-503, 2006.

Digital Library

Cited By

Ie EJain VWang JNarvekar SAgarwal RWu RCheng HChandra TBoutilier C(2019)SLATEQProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367400(2592-2599)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367400
Tabakhi AYeoh WYokoo MElkind EVeloso MAgmon NTaylor M(2019)Parameterized Heuristics for Incomplete Weighted CSPs with Elicitation CostsProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331729(476-484)Online publication date: 8-May-2019
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Dragone PTeso SPasserini AMcIlraith SWeinberger K(2018)Constructive preference elicitation over hybrid combinatorial spacesProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504394(2943-2950)Online publication date: 2-Feb-2018
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Index Terms

Optimal Bayesian recommendation sets and myopically optimal choice query sets
1. Information systems
  1. Information retrieval
2. Theory of computation

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Guide Proceedings

NIPS'10: Proceedings of the 24th International Conference on Neural Information Processing Systems - Volume 2

December 2010

2630 pages

Publisher

Curran Associates Inc.

Red Hook, NY, United States

Publication History

Published: 06 December 2010

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Cited By

Ie EJain VWang JNarvekar SAgarwal RWu RCheng HChandra TBoutilier C(2019)SLATEQProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367400(2592-2599)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367400
Tabakhi AYeoh WYokoo MElkind EVeloso MAgmon NTaylor M(2019)Parameterized Heuristics for Incomplete Weighted CSPs with Elicitation CostsProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331729(476-484)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331729
Dragone PTeso SPasserini AMcIlraith SWeinberger K(2018)Constructive preference elicitation over hybrid combinatorial spacesProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504394(2943-2950)Online publication date: 2-Feb-2018
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Le TTabakhi ATran-Thanh LYeoh WSon TAndre EKoenig SDastani MSukthankar G(2018)Preference Elicitation with Interdependency and User Bother CostProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237918(1459-1467)Online publication date: 9-Jul-2018
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Dragone PPellegrini GVescovi MTentori KPasserini APera SEkstrand MAmatriain XO'Donovan J(2018)No more ready-made dealsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240348(163-171)Online publication date: 27-Sep-2018
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Dragone P(2017)Constructive recommendationProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171837.3172042(5177-5178)Online publication date: 19-Aug-2017
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Dragone PCremonesi PRicci FBerkovsky STuzhilin A(2017)Constructive RecommendationProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109867(441-445)Online publication date: 27-Aug-2017
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Benabbou NPerny PViappiani P(2017)Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problemsArtificial Intelligence10.1016/j.artint.2017.02.001246:C(152-180)Online publication date: 1-May-2017
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Teso SPasserini AViappiani P(2016)Constructive preference elicitation by setwise max-margin learningProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060910(2067-2073)Online publication date: 9-Jul-2016
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Pigozzi GTsoukiàs AViappiani P(2016)Preferences in artificial intelligenceAnnals of Mathematics and Artificial Intelligence10.1007/s10472-015-9475-577:3-4(361-401)Online publication date: 1-Aug-2016
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