Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
This addresses a computational bottleneck for researchers and practitioners using GCNs in large-scale recommendation systems, but it is incremental as it builds on existing GCN methods.
The paper tackled the computational expense and oversimplification issues in Graph Convolutional Networks (GCNs) for large graphs in recommendation systems by proposing a Low-pass Collaborative Filter (LCF) that leverages original graph convolution to remove noise and reduce complexity, resulting in significant improvements in effectiveness and efficiency over existing GCNs.
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution 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 \textit{original graph convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative \textbf{F}ilter (\textbf{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 graph convolution in an unscathed way. Experiments show that LCF improves the effectiveness and efficiency of graph convolution and our GCN outperforms existing GCNs significantly. Codes are available on \url{https://github.com/Wenhui-Yu/LCFN}.