Abstract
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%.
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© 2008 Springer-Verlag Berlin Heidelberg
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Qin, M., Buffett, S., Fleming, M.W. (2008). Predicting User Preferences Via Similarity-Based Clustering. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_22
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DOI: https://doi.org/10.1007/978-3-540-68825-9_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68821-1
Online ISBN: 978-3-540-68825-9
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