Constrained Graph Convolution Networks Based on Graph Enhancement for Collaborative Filtering

Abstract

Graph Convolutional Networks (GCNs) have gained much attention and have achieved excellent performance in many graph-based collaborative filtering (CF) tasks in recent years. Its success relies on a fundamental assumption that the original graph structure is reliable and consistent with the properties of GNNs. However, most original graphs can seriously impair model performance due to noise and data sparsity problems. In addition, for large user-item graphs, the explicit message passing in traditional GCNs slows down the convergence speed during training and weakens the training efficiency of the model. Based on this, we propose Constrained Graph Convolution Networks Based on Graph Enhancement for Collaborative Filtering (EL-GCCF). The graph initialization learning module integrates the structural and feature information in the graph by generating two graph structures. It enhances the original graph and effectively mitigates the noise problem. Second, the multi-task constrained graph convolution skips explicit message passing. It effectively mitigates the over-smoothing problem in training and improves the training efficiency of the model by using an auxiliary sampling strategy. Experimental results on two real datasets show that the EL-GCCF model outperforms many mainstream models and has higher training efficiency.