Integrating Dual User Network Embedding with Matrix Factorization for Social Recommender Systems

Abstract

To address the data sparsity problem faced by recommender systems, social network among users is often utilized to complement rating data for improving the recommendation performance. One of current trends is to combine the idea of matrix factorization (MF) for predicting ratings with the idea of graph embedding (GE) for analyzing social network towards recommendation tasks. Despite enjoying many advantages, the existing integrated models have two critical limitations. First, such models are designed to work with either explicit or implicit social network, but little is known in taking both into account. Second, the users’ embeddings learned by GE are fed to the downstream MF, but not reverse, which is sub-optimal because rating information is not considered for learning the users’ embeddings. In this paper, we propose a novel social recommendation algorithm which exploits both explicit and implicit social networks towards the task of rating prediction. In Particular, we seamlessly integrate MF model and GE model within a unified optimization framework, in which MF and GE tasks can be reinforced each other during the learning process. Our encouraging experimental results on three real-world benchmarks validate the superiority of the proposed approach to state-of-the-art social recommendation methods.