Predicting User Preferences via Similarity-Based Clustering

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TypeArticle
ConferenceCanadian Artificial Intelligence Conference (AI 2008), May 27-30, 2008., Windsor, Ontario, Canada
AbstractThis 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%.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number50333
NPARC number8913520
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Record identifieracf0d9e0-0d11-4734-9b23-2c158831a61c
Record created2009-04-22
Record modified2016-05-09
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