SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
mTrust: discerning multi-faceted trust in a connected world
Proceedings of the fifth ACM international conference on Web search and data mining
Circle-based recommendation in online social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Traditionally, trust-aware recommendation methods that utilize trust relations for recommender systems assume a single type of trust between users. However, this assumption ignores the fact that trust as a social concept inherently has many aspects. A user may place trust differently to different people. Motivated by this observation, we propose a novel probabilistic factor analysis method, which learns the multi-faceted trust relations and user profiles through a shared user latent feature space. Experimental results on the real product rating data set show that our approach outperforms state-of-the-art methods on the RMSE measure.