Graph Neural Network Recommendation Method Based on User Behavior

Abstract

In recent years, the recommendation field has gradually started to combine GNN-like approaches to address the challenges. The Neural Graph Collaborative Filtering (NGCF) framework has made a preliminary attempt to extract structural knowledge in model-based collaborative filtering based on graph convolution with message passing mechanisms, opening up new research possibilities. However, the NGCF framework does not consider the semantic information in the topology and only constructs a single heterogeneous graph. In our work, we suggest explicit semantic encoding of edges for different user behaviors and propose a Heterogeneous Graph Convolution Collaborative Filtering (HGCCF) framework combined with message propagation mechanism, which can mine richer collaborative information and effectively alleviate the sparsity problem of bipartite graph and enhance the cold start capability. Furthermore, we reduce the computational effort through compressing the initial embedding vector and sharing parameters in the message passing. Our Top-N recommendation experiments on pre-processed real e-commerce data from Alibaba verify that HGCCF has higher recommendation accuracy and the ability to cope with cold starts. In addition, we also design hyperparametric experiments of HGCCF to explore the effect of HGCCF on performance with different propagation learning layers, different normalization coefficients prui, and different output dimensions of embedding propagation layers.