GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Sequence-Based Trust for Document Recommendation
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Sequence-based trust in collaborative filtering for document recommendation
International Journal of Human-Computer Studies
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Knowledge-Based Systems
ACM Transactions on the Web (TWEB)
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Collaborative filtering (CF) technique has been widely used in recommending items of interest to users based on social relationships. The notion of trust is emerging as an important facet of relationships in social networks. In this paper, we present an improved mechanism to the standard CF techniques by incorporating trust into CF recommendation process. We derive the trust score directly from the user rating data and exploit the trust propagation in the trust web. The overall performance of our trust-based recommender system is presented and favorably compared to other approaches.