Improving top-K recommendation with truster and trustee relationship in user trust network

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Due to the data sparsity problem, social network information is often additionally used to improve the performance of recommender systems. While most existing works exploit social information to reduce the rating prediction error, e.g., RMSE, a few had aimed to improve the top-k ranking prediction accuracy. This paper proposes a novel top-k ranking oriented recommendation method, TRecSo, which incorporates social information into recommendation by modeling two different roles of users as trusters and trustees while considering the structural information of the network. Empirical studies on real-world datasets demonstrate that TRecSo leads to a remarkable improvement compared with previous methods in top-k recommendation. (C) 2016 Elsevier Inc. All rights reserved.
Publisher
ELSEVIER SCIENCE INC
Issue Date
2016-12
Language
English
Article Type
Article
Citation

INFORMATION SCIENCES, v.374, pp.100 - 114

ISSN
0020-0255
DOI
10.1016/j.ins.2016.09.024
URI
http://hdl.handle.net/10203/278028
Appears in Collection
IE-Journal Papers(저널논문)
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