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.