Social information is usually jointly utilized with rating information to help the traditional recommendation system providing more personalized services, while how to make full use of social information to build better recommendation models still faces lots of challenges. In this paper, we propose a novel social recommendation model taking advantage of both deep and shallow model, with deep autoencoders acting as the nonlinear feature extractor and MF-based method being used to depict the user's preferences. Then, motivated by the idea of word2vec, we provide an appealing method that embeds users into latent space and meanwhile preserves the structural information of social networks during the embedding. Also, the embedded latent features of each user are corresponding to the dual roles the user plays in the recommendation. Furthermore, we design a loss function for the holistic training of the model, and our loss function is made up mainly of three parts which embody the effects of different factors on the rating predictions. Specifically, the loss function (i) captures the personal preference from user-item adoption matrix based on matrix factorization, (ii) discriminates the two different social functions of users and further evaluates the effects of interpersonal influence through user embedding and social influence matrix, (iii) and avoids overfitting by imposing a quadratic regularization penalty. As a result, our model can predict the missing ratings with the MF-based method by consuming the latent features of users and items extracted by the deep model. The experiments show that our method outperforms existing methods and performs well on cold start users. Social recommendation, user embedding, dual role, auto-encoder.
INDEX TERMS
This paper studied the static and dynamic characteristics of the real social networks as well as their proposed generative models, among which the Butterfly Model [1] is useful while not being flexible enough to generate the social networks with the expected power-law exponent. Therefore, a novel Flexible Butterfly Model (FBM) is proposed based on the Butterfly Model and combined with the Monte Carlo method and a Bayesian Graph Model for the training of the FBM Model is built in order to learn parameters from real social networks. Experiments have shown that the FBM model can adjust the law power exponent of the generated social network effectively by the introduced parameters. Meanwhile, the FBM model also maintains the vast majority of important characteristics that the Butterfly model has.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.