Multi-interaction fusion collaborative filtering for social recommendation

Abstract

GNNs(Graph Neural Networks) use graph structure to make recommendations, receiving more and more attention. Firstly, existing work focuses on aggregating social interaction information, ignoring users who rate on the same items. Secondly, the existing recommendation methods cannot dynamically reflect changes in user interests. Thirdly, existing methods do not take into account the interaction of subgraphs in GNNs and interactions between user and item factors. In this paper, a graph social fusion recommendation (GSFR) method is proposed. GSFR captures multiple social information simultaneously, based on which it can dynamically adjust user interest weight. Specifically, GSFR captures the interactions in subgraphs with a dynamic attention mechanism, which can represent changes in user interests in heterogeneous networks. A mutualistic mechanism is combined to simulate the mutually reinforcing relationship between social behavior and virtual behavior. User and item latent factors are obtained based on space vectors from the aggregation part. Recommendations are made from inherent characteristics of space vectors interaction behaviors. Comprehensive experimental results on three public datasets show the effectiveness of the proposed model.