Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Authors

Wenhui Yu, Zheng Qin

Abstract

<p>Graph Convolutional Neural Network (GCN) is widely used in graph data learning tasks such as recommendation. When facing to a large graph, the graph convolution is very computational expensive thus is simplified in all existing GCNs, while 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 in observed data, and it also reduces the complexity of graph convolution without hurting its ability. Experiments show that LCF improves the effectiveness and efficiency of graph convolution and our GCN outperforms existing GCNs significantly.</p>