Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

Jul 12, 2020



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.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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