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Learning rating patterns for Top-N recommendations
conference contribution
posted on 2012-01-01, 00:00 authored by Yongli Ren, Gang LiGang Li, Wanlei ZhouTwo rating patterns exist in the user × item rating matrix and influence each other: the personal rating patterns are hidden in each user's entire rating history, while the global rating patterns are hidden in the entire user × item rating matrix. In this paper, a Rating Pattern Subspace is proposed to model both of the rating patterns simultaneously by iteratively refining each other with an EM-like algorithm. Firstly, a low-rank subspace is built up to model the global rating patterns from the whole user × item rating matrix, then, the projection for each user on the subspace is refined individually based on his/her own entire rating history. After that, the refined user projections on the subspace are used to improve the modelling of the global rating patterns. Iteratively, we can obtain a well-trained low-rank Rating Pattern Subspace, which is capable of modelling both the personal and the global rating patterns. Based on this subspace, we propose a RapSVD algorithm to generate Top-N recommendations, and the experiment results show that the proposed method can significantly outperform the other state-of-the-art Top-N recommendation methods in terms of accuracy, especially on long tail item recommendations.
History
Event
Advances in Social Networks Analysis and Mining. Conference (2012 : Istanbul, Turkey)Pagination
472 - 479Publisher
IEEE Computer SocietyLocation
Istanbul, TurkeyPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-08-26End date
2012-08-29ISBN-13
9780769547992Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ASONAM 2012 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningUsage metrics
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