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The primary purpose of recommender system is providing valuable and pertinent information actively according to users' preference. Collaborative filtering (CF) is a successful approach among different recommendation techniques. However, CF-based methods usually suffer from limited performance due to the sparsity of rating data. To address such issues, utilizing auxiliary information such as content information and social networks is a very promising approach. This paper proposes an effective Bayesian hierarchical framework called Deep Trust Recommender (DTR), which aims to improve the quality of recommender systems by integrating user-item rating information and social trust network into the embedding representation. Specifically, we first apply deep learning methods like stacked denoising auto-encoders to learn the approximation representation of the users preference from the users trust network …