Graph Attention Social Recommendation Model Based on Similar Item Graph

Abstract

Social recommendation improves the performance of recommendation systems by mitigating the effects of data sparsity on user prediction through the social attributes in users' social networks. However, existing social recommendation models often use user-item interaction graphs to construct item feature vectors, ignoring the effects of similar items. In addition, the correlation between feature vectors is not considered when using inner products of elements to model the interaction between user and item feature vectors. In this paper, we propose a graph attention social recommendation model based on similar item graphs (SIG-GATSR), initially enabling the user-item interaction graph, social relationship graph, and similar item graphs to transfer information on the network through an improved graph attention network, and then constructing item feature vectors by combining similarity. Subsequently, we explicitly model the correlation between user and item feature vectors by performing the outer product operation. The final rating prediction is then obtained by modeling the interaction using multilayer convolutional neural networks. Detailed experimental results of Ciao, Epinions and Flixter showed that the proposed social recommendation model outperforms other related models in terms of prediction accuracy and normalized discounted cumulative gain (NDCG).