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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 Zhang
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.

History

Pagination

975-978

Location

Gold Coast, Queensland

Start date

2014-07-06

End date

2014-07-11

ISBN-13

9781450322591

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2014, Association for Computing Machinery

Editor/Contributor(s)

[Unknown]

Title of proceedings

SIGIR 2014 : Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

Event

Research and Development in Information Retrieval. Conference (37th : 2014 : Gold Coast, Queensland)

Publisher

Association for Computing Machinery

Place of publication

New York, NY

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