Optimal Bayesian recommendation sets and myopically optimal choice query sets

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Viappiani, Paolo ^a   Boutilier, Craig ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS;

BAYESIAN; BAYESIAN APPROACHES; EXPECTED UTILITY; EXPECTED VALUE OF INFORMATION; OPTIMAL CHOICE; OPTIMISATIONS; SET OPTIMIZATIONS; SIMPLE++; USER SELECTION; UTILITY ELICITATIONS;

OPTIMIZATION;

EID: 85162076891     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Ali Abbas. Entropy methods for adaptive utility elicitation. IEEE Transactions on Systems, Science and Cybernetics, 34(2):169-178, 2004.
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.
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.
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.
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.
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.
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.
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.
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.
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.
15. Jordan J. Louviere, David A. Hensher, and Joffre D. Swait. Stated Choice Methods: Analysis and Application. Cambridge University Press, Cambridge, 2000.
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.
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.
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.
21. James Reilly, Kevin McCarthy, Lorraine McGinty, and Barry Smyth. Incremental critiquing. Knowledge-Based Systems, 18(4-5):143-151, 2005.
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.
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.