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Modeling dual role preferences for trust-aware recommendation

Yao, Weilong, He, Jing, Huang, Guangyan and Zhang, Yanchun 2014, Modeling dual role preferences for trust-aware recommendation, in SIGIR 2014 : Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, NY, pp. 975-978, doi: 10.1145/2600428.2609488.

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Title Modeling dual role preferences for trust-aware recommendation
Author(s) Yao, Weilong
He, Jing
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Zhang, Yanchun
Conference name Research and Development in Information Retrieval. Conference (37th : 2014 : Gold Coast, Queensland)
Conference location Gold Coast, Queensland
Conference dates 6-11 Jul. 2014
Title of proceedings SIGIR 2014 : Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
Editor(s) [Unknown]
Publication date 2014
Conference series Research and Development in Information Retrieval. Conference
Start page 975
End page 978
Total pages 4
Publisher Association for Computing Machinery
Place of publication New York, NY
Keyword(s) Collaborative filtering
Dual role
Matrix factorization
Network structure
Summary Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. Copyright 2014 ACM.
ISBN 9781450322591
Language eng
DOI 10.1145/2600428.2609488
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 810105 Intelligence
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Association for Computing Machinery
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070658

Document type: Conference Paper
Collection: School of Information Technology
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