Proceedings of the 10th international conference on Intelligent user interfaces
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to recommend with trust and distrust relationships
Proceedings of the third ACM conference on Recommender systems
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
A simple but effective method to incorporate trusted neighbors in recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Proceedings of the 7th ACM conference on Recommender systems
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Knowledge-Based Systems
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Social trust holds great potential for improving recommendation and much recent work focuses on the use of social trust for rating prediction, in particular, in the context of the Epinions dataset. An experimental comparison with trust-free, naïve approaches suggests that state-of-the-art social-trust-aware recommendation approaches, in particular Social Trust Ensemble (STE), can fail to isolate the true added value of trust. We demonstrate experimentally that not only trust-set users, but also random users can be exploited to yield recommendation improvement via STE. Specific users, however, do benefit from use of social trust, and we conclude with an investigation of their characteristics.