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Enhanced SVD for collaborative filtering
conference contribution
posted on 2016-01-01, 00:00 authored by X Guan, Chang-Tsun LiChang-Tsun Li, Y GuanMatrix factorization is one of the most popular techniques for prediction problems in the fields of intelligent systems and data mining. It has shown its effectiveness in many real-world applications such as recommender systems. As a collaborative filtering method, it gives users recommendations based on their previous preferences (or ratings). Due to the extreme sparseness of the ratings matrix, active learning is used for eliciting ratings for a user to get better recommendations. In this paper, we propose a new matrix factorization model called Enhanced SVD (ESVD) which combines the classic matrix factorization method with a specific rating elicitation strategy. We evaluate the proposed ESVD method on the Movielens data set, and the experimental results suggest its effectiveness in terms of both accuracy and efficiency, when compared with traditional matrix factorization methods and active learning methods.
History
Event
Pacific Asia Knowledge Discovery and Data Mining. Conference (20th : 2016 : Auckland, N.Z.)Volume
9652Series
Pacific Asia Knowledge Discovery and Data Mining ConferencePagination
503 - 514Publisher
SpringerLocation
Auckland, N.Z.Place of publication
Cham, SwitzerlandPublisher DOI
Start date
2016-04-19End date
2016-04-22ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319317496Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2016, Springer International Publishing SwitzerlandEditor/Contributor(s)
J Bailey, L Khan, T Washio, G Dobbie, J Huang, R WangTitle of proceedings
PAKDD 2016 : Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining 2016Usage metrics
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