Integrating Social Circles and Network Representation Learning for Item Recommendation
Yonghong Yu, Qiang Wang, Zhang Li, Can Wang, Sifan Wu, Boyu Qi, Xiaotian Wu · 2019
With the ever increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize the trust relationships of users to improve the performance of recommendation algorithms. However, most of the existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates the social circles and the network representation learning for item recommendation. Specifically, we firstly infer the domain-specific social trust circles based on the original users’ rating information and the social network information. Next, we adopt the network representation technique to embed the domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships with the domain-specific matrix factorization model to learn the latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms the traditional social-network-based recommendation algorithms.