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Learning user preference patterns for Top-N recommendations

Ren,Y, Li,G and Zhou,W 2012, Learning user preference patterns for Top-N recommendations, in IEEE/WIC/ACM 2012 : Proceedings from the International Conference on International Conferences on Web Intelligence and Intelligent Agent Technology, IEEE, Piscataway, N.J., pp. 137-144, doi: 10.1109/WI-IAT.2012.102.

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Title Learning user preference patterns for Top-N recommendations
Author(s) Ren,Y
Li,GORCID iD for Li,G orcid.org/0000-0003-1583-641X
Zhou,WORCID iD for Zhou,W orcid.org/0000-0002-1680-2521
Conference name Web Intelligence and Intelligent Agent Technology. Joint Conferences (2012 : Macau, China)
Conference location Macau, China
Conference dates 4-7 Dec. 2012
Title of proceedings IEEE/WIC/ACM 2012 : Proceedings from the International Conference on International Conferences on Web Intelligence and Intelligent Agent Technology
Editor(s) [Unknown]
Publication date 2012
Conference series Web Intelligence and Intelligent Agent Technology Joint Conferences
Start page 137
End page 144
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Pattern Recognition
Top-N recommendations
Summary In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation- Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N recommendation in terms of accuracy. © 2012 IEEE.
ISBN 9780769548807
Language eng
DOI 10.1109/WI-IAT.2012.102
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
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067478

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