Heterogeneous Social Recommendation Model With Network Embedding

Due to the number of users and items increasing sharply, data sparsity has become an extremely serious problem for recommendation systems. Social relations consist of complex and rich information, which have a good alleviation effect on sparsity problems. Heterogeneous Information Network (HIN) is excellent in modeling the complex and structural information. Hence, we integrate HIN into the social recommendation. In this paper, we propose a model named Heterogeneous Social Recommendation model with Network Embedding (HSR). The social relations are divided into direct social relations and indirect social relations. We design a novel social influence calculation method to evaluate the influence of direct social relations. Based on the heterogeneous information network embedding method, we represent indirect social relations as feature embeddings and transform the learned embeddings into user-item feature interaction matrix by outer product. The final item list for a user is generated by the method of the convolutional neural network combined with the list of items generated by direct social relations. Extensive experiments on three real-world datasets show significant improvements of our proposed method over state-of-the-art methods. Additionally, experiments show that using heterogeneous network embedding can obtain better recommendation performance.