GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
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Proceedings of the 13th international conference on World Wide Web
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ACM Transactions on Internet Technology (TOIT)
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
A protocol to preserve a code of conduct
Proceedings of the 2007 ACM symposium on Applied computing
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Proceedings of the 2008 ACM symposium on Applied computing
Key figure impact in trust-enhanced recommender systems
AI Communications - Recommender Systems
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Social Trust-Aware Recommendation System: A T-Index Approach
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Source credibility model for neighbor selection in collaborative web content recommendation
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Mechanizing social trust-aware recommenders with T-index augmented trustworthiness
TrustBus'10 Proceedings of the 7th international conference on Trust, privacy and security in digital business
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International Journal of Human-Computer Studies
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DBSocial '12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks
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Personal and Ubiquitous Computing
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Information Sciences: an International Journal
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Expert Systems with Applications: An International Journal
ACM Transactions on the Web (TWEB)
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Despite its success, similarity-based collaborative filtering suffers from some significant limitations, such as scalability and sparsity. This paper introduces trust to the domain of collaborative filtering to overcome these limitations. Compared with the similarity-based CF, introduction of trust does improve the performance of CF in terms of coverage, prediction accuracy, and robustness in the presence of attacks. Experimental results based on a real dataset are illustrated as evidences to support our claim.