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Modeling dual role preferences for trust-aware recommendation
conference contribution
posted on 2014-01-01, 00:00 authored by W Yao, J He, Guangyan HuangGuangyan Huang, Y ZhangUnlike 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.
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Research and Development in Information Retrieval. Conference (37th : 2014 : Gold Coast, Queensland)Pagination
975 - 978Publisher
Association for Computing MachineryLocation
Gold Coast, QueenslandPlace of publication
New York, NYPublisher DOI
Start date
2014-07-06End date
2014-07-11ISBN-13
9781450322591Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, Association for Computing MachineryEditor/Contributor(s)
[Unknown]Title of proceedings
SIGIR 2014 : Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information RetrievalUsage metrics
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