Abstract: The existing implicit feedback collaborative algorithms directly use sparse binary social trust information to assist in recommendation, which has serious data sparseness problems. Besides, there is no impact of deep integration of social trust information. Aiming at the above problems, an algorithm is proposed, which uses the denoising autoencoder to deeply fuse the user's implicit feedback data and social information. Firstly, we distinguish the trust of users from different angles, propose a new measurement method of trust similarity, and improve the sparseness of social trust data. Secondly, denoising autoencoders are used to deeply fuse trust data and user implicit interactive information. By combining the advantages of the two, the recommendation quality is effectively improved. Experiments show that this method is superior to the existing mainstream implicit feedback recommendation algorithms.

Key words: recommendation system, social network, deep learning, denoising autoencoder, implicit feedback