A learning method for Top-N recommendations with incomplete data

Ren, Yongli, Li, Gang and Zhou, Wanlei 2013, A learning method for Top-N recommendations with incomplete data, Social network analysis and mining, vol. 3, no. 4, pp. 1135-1148, doi: 10.1007/s13278-013-0103-2.

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Title A learning method for Top-N recommendations with incomplete data
Author(s) Ren, Yongli
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Journal name Social network analysis and mining
Volume number 3
Issue number 4
Start page 1135
End page 1148
Total pages 14
Publisher Springer
Place of publication Berlin, Germany
Publication date 2013-12
ISSN 1869-5450
Keyword(s) rating patterns
Top-N recommendations
Summary In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-N recommendations. Previous research ignore a fact that two rating patterns exist in the user × item rating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can consequently be exploited to improve the modelling of rating patterns, especially when the user × item rating matrix is highly incomplete due to the well-known data sparsity issue. This paper proposes a Rating Pattern Subspace to iteratively re-optimize the missing values in each user’s rating history by modelling both the global and the personal rating patterns simultaneously. The basic idea is to project the user × item rating matrix on a low-rank subspace to capture the global rating patterns. Then, the projection of each individual user on the subspace is further optimized according to his/her own rating history and the captured global rating patterns. Finally, the optimized user projections are used to improve the modelling of the global rating patterns. Based on this subspace, we propose a RapSVD-L algorithm for Top-N recommendations. In the experiments, the performance of the proposed method is compared with the state-of-the-art Top-N recommendation methods on two real datasets under various data sparsity levels. The experimental results show that RapSVD-L outperforms the compared algorithms not only on the all items recommendations but also on the long tail item recommendations in terms of accuracy.
Language eng
DOI 10.1007/s13278-013-0103-2
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 ©2013, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083350

Document type: Journal Article
Collection: School of Information Technology
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