Learning rating patterns for Top-N recommendations

Ren, Yongli, Li, Gang and Zhou, Wanlei 2012, Learning rating patterns for Top-N recommendations, in ASONAM 2012 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, Piscataway, N.J., pp. 472-479.

Attached Files
Name Description MIMEType Size Downloads

Title Learning rating patterns for Top-N recommendations
Author(s) Ren, Yongli
Li, Gang
Zhou, Wanlei
Conference name Advances in Social Networks Analysis and Mining. Conference (2012 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 26-29 Aug. 2012
Title of proceedings ASONAM 2012 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Editor(s) [Unknown]
Publication date 2012
Conference series Advances in Social Networks Analysis and Mining Conference
Start page 472
End page 479
Total pages 8
Publisher IEEE Computer Society
Place of publication Piscataway, N.J.
Keyword(s) rating patterns
Top-N recommendations
Summary Two 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.
ISBN 9780769547992
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051354

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 23 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Mon, 18 Mar 2013, 09:41:22 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.