Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

Graph Convolutional Network (GCN) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the <PRE_TAG>graph convolution</POST_TAG> is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the original graph convolution in GCN and propose a Low-pass Collaborative Filter (LCF) to make it applicable to the large graph. LCF is designed to remove the noise caused by exposure and quantization in the observed data, and it also reduces the complexity of <PRE_TAG>graph convolution</POST_TAG> in an unscathed way. Experiments show that LCF improves the effectiveness and efficiency of <PRE_TAG>graph convolution</POST_TAG> and our GCN outperforms existing GCNs significantly. Codes are available on https://github.com/Wenhui-Yu/LCFN.