Recommendation with Social Relationships via Deep Learning
Dimitrios Rafailidis, Fábio Crestani · 2017
While users trust the selections of their social friends in recommendation systems, the preferences of friends do not necessarily match. In this study, we introduce a deep learning approach to learn both about user preferences and the social influence of friends when generating recommendations. In our model we design a deep learning architecture by stacking multiple marginalized Denoising Autoencoders. We define a joint objective function to enforce the latent representation of social relationships in the Autoencoder's hidden layer to be as close as possible to the users' latent representation when factorizing the user-item matrix. We formulate a joint objective function as a minimization problem to learn both user preferences and friends' social influence and we present an optimization algorithm to solve the joint minimization problem. Our experiments on four benchmark datasets show that the proposed approach achieves high recommendation accuracy, compared to other state-of-the-art methods.