  When a user connects to the Internet to fulfill his needs, he often
encounters a huge amount of related information. Recommender systems are the
techniques for massively filtering information and offering the items that
users find them satisfying and interesting. The advances in machine learning
methods, especially deep learning, have led to great achievements in
recommender systems, although these systems still suffer from challenges such
as cold-start and sparsity problems. To solve these problems, context
information such as user communication network is usually used. In this paper,
we have proposed a novel recommendation method based on Matrix Factorization
and graph analysis methods. In addition, we leverage deep Autoencoders to
initialize users and items latent factors, and deep embedding method gathers
users' latent factors from the user trust graph. The proposed method is
implemented on two standard datasets. The experimental results and comparisons
demonstrate that the proposed approach is superior to the existing
state-of-the-art recommendation methods. Our approach outperforms other
comparative methods and achieves great improvements.
