An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
Utilities as Random Variables: Density Estimation and Structure Discovery
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Understanding and improving automated collaborative filtering systems
Understanding and improving automated collaborative filtering systems
Compact value-function representations for qualitative preferences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Regret-based utility elicitation in constraint-based decision problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Product recommendation with interactive query management and twofold similarity
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Toward case-based preference elicitation: similarity measures on preference structures
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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The enormous number of questions needed to acquire a full preference model when the size of the outcome space is large forces us to work with partial models that approximate the user's preferences. In this way we must devise elicitation strategies that focus on the most important questions and at the same time do not need to enumerate the outcome space. In this paper we focus on adaptive elicitation of GAI-decomposable preferences for top-k recommendation tasks in large combinatorial domains. We propose a method that interleaves the generation of top-k solutions with a heuristic selection of questions for refining the user preference model. Empirical results for a large combinatorial problem are given.