| Download | - View accepted manuscript: Predicting User Preferences via Similarity-Based Clustering (PDF, 284 KiB)
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| Author | Search for: Qin, M.; Search for: Buffett, Scott; Search for: Fleming, Michael |
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| Format | Text, Article |
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| Conference | Canadian Artificial Intelligence Conference (AI 2008), May 27-30, 2008, Windsor, Ontario, Canada |
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| 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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| Publication date | 2008 |
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| In | |
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| Language | English |
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| NRC number | NRCC 50333 |
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| NPARC number | 8913520 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | acf0d9e0-0d11-4734-9b23-2c158831a61c |
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| Record created | 2009-04-22 |
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| Record modified | 2020-08-12 |
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