Modelling personal preferences for Top-N movie recommendations

Ren, Yongli, Li, Gang and Zhou, Wanlei 2014, Modelling personal preferences for Top-N movie recommendations, Web intelligence, vol. 12, no. 3, pp. 289-307, doi: 10.3233/WIA-140297.

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Title Modelling personal preferences for Top-N movie recommendations
Author(s) Ren, Yongli
Li, GangORCID iD for Li, Gang
Zhou, WanleiORCID iD for Zhou, Wanlei
Journal name Web intelligence
Volume number 12
Issue number 3
Start page 289
End page 307
Total pages 18
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2014
ISSN 2405-6456
Keyword(s) temporal preferences
movie recommended system
Top-N recommendations
Summary Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. 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 movie recommendations. 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 movie recommendations in terms of accuracy.
Language eng
DOI 10.3233/WIA-140297
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, IOS Press
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