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Improving Top-N recommendations with user consuming profiles
conference contribution
posted on 2012-01-01, 00:00 authored by Yongli Ren, Gang LiGang Li, Wanlei ZhouIn this work, we observe that user consuming styles tend to change regularly following some profiles. Therefore, we propose a consuming profile model to capture the user consuming styles, then apply it to improve the Top-N recommendation. The basic idea is to model user consuming styles by constructing a representative subspace. Then, a set of candidate items can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results show that the proposed model can improve the accuracy of Top-N recommendations much better than the state-of-the-art algorithms.
History
Event
Pacific Rim International Conference on Artificial Intelligence (12th : 2012 : Kuching, Malaysia)Source
PRICAI 2012 : trends in artificial intelligence : 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia September 3-7 2012 : proceedingsSeries
Lecture Notes in Computer Science ; v.7458Pagination
887 - 890Publisher
Springer-VerlagLocation
Kuching, MalaysiaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2012-09-03End date
2012-09-07ISSN
0302-9743ISBN-13
9783642326943Language
engPublication classification
E1 Full written paper - refereedExtent
95Editor/Contributor(s)
P Anthony, M Ishizuka, D LukoseTitle of proceedings
PRICAI 2012 : Trends in Artificial Intelligence : 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia September 3-7 2012 : proceedingsUsage metrics
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