Graph Attention Networks for Neural Social Recommendation

In recent years, social recommendation is a research hotspot because it contains social network information which can effectively solve the problem of data sparsity and cold start. But the social recommendation task faces two problems: one is that how to accurately learn user latent vector and item latent vector from user-item interaction graph and social graph, the other is that how to depict the intrinsic and complex interaction between users and items. With the development of graph neural networks, node embedding is becoming more and more accurate on the graph. Besides neural collaborative filtering explores the interaction of users and items deeply. So in this paper, we propose a novel model: graph attention networks for neural social recommendation (GAT-NSR). This model adopts multi-head attention mechanism for message passing on the two graphs, which get user & item latent vector from different perspectives. And also we design a neural collaborative recommendation module to capture the inherent characteristics of user-item interaction behavior for recommendation. Finally, detailed experimental results on two real-world datasets clearly prove the effectiveness of our proposed model.