Higher-Order Smoothness Enhanced Graph Collaborative Filtering

Abstract

Graph Neural Networks (GNNs) based recommendations have shown significant performance improvement by explicitly modeling the user-item interactions as a bipartite graph. However, the existing GNNs-based recommendation methods suffer from the over-smoothing problem caused by utilizing the uniform distance of the reception field. To address this issue, we propose to explicitly incorporate the higher-order smoothness information into the node representation learning, and propose a new GNNs-based recommendation model named H igher-order S moothness enhanced G raph C ollaborative F iltering (HS-GCF). The proposed model is mainly composed of two parts, namely lower-order module and higher-order module. The lower-order module guarantees that the lower-order smoothness is well obtained by using the user-item interactions. The higher-order module uses the latent group assumption to restrict too much noise introduced by the uniform distance property, which we call the higher-order smoothness information. Experiments are conducted on three real-world public datasets, and the experimental results show the performance improvements compared with several state-of-the-art methods and verify the importance of explicitly incorporating the higher-order smoothness information into the node representation learning.