Heterogeneous information fusion based graph collaborative filtering recommendation

Abstract

Nowadays, with the application of 5G, graph-based recommendation algorithms have become a research hotspot. Graph neural networks encode the graph structure information in the node representation through an iterative neighbor aggregation method, which can effectively alleviate the problem of data sparsity. In addition, more and more information graph can be used in collaborative filtering recommendation, such as user social information graph, user or item attributed information graph, etc. In this paper, we propose a novel heterogeneous information fusion based graph collaborative filtering method, which models graph data from different heterogeneous graph, and combines them together to enhance presentation learning. Through information propagation and aggregation, our model can learn the latent embeddings effectively and enhance the performance of recommendation. Experimental results on different datasets validate the outperformance of the proposed framework.