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
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
Detecting opponent concessions in multi-issue automated negotiation
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
A Bayesian classifier for learning opponents' preferences in multi-object automated negotiation
Electronic Commerce Research and Applications
Regret-based utility elicitation in constraint-based decision problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Comparing apples and oranges through partial orders: an empirical approach
ACC'09 Proceedings of the 2009 conference on American Control Conference
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This paper explores the idea of clustering partial preference relations as a means for agent prediction of users' preferences. Due to the high number of possible outcomes in a typical scenario, such as an automated negotiation session, elicitation techniques can provide only a sparse specification of a user's preferences. By clustering similar users together, we exploit the notion that people with common preferences over a given set of outcomes will likely have common interests over other outcomes. New preferences for a user can thus be predicted with a high degree of confidence by examining preferences of other users in the same cluster. Experiments on the MovieLens dataset show that preferences can be predicted independently with 70-80% accuracy. We also show how an error-correcting procedure can boost accuracy to as high as 98%.