| Autor: |
Qin, Mian, Buffett, Scott, Fleming, Michael W. |
| Zdroj: |
Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian Ai 2008 Windsor, Canada, May 28-30, 2008 Proceedings; 2008, p222-233, 12p |
| Abstrakt: |
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%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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