论文标题
图形卷积网络用于推荐使用低通滤波器
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
论文作者
论文摘要
\ textbf {g} raph \ textbf {c} onvolutional \ textbf {n} etwork（\ textbf {gcn}）被广泛用于图形数据学习任务，例如建议。但是，当面对大图时，图表卷积在计算上非常昂贵，因此在所有现有的GCN中都简化了，但是由于过度简化而受到严重损害。要解决此差距，我们利用GCN中的\ textIt {原始图形卷积}，并提出了A \ textbf {l} ow-pass \ textbf {c} ollaborative \ textbf {f textbf {f} ilter（\ textbf {lcf}），以使其适用于大图。 LCF旨在消除观察到的数据中暴露和量化引起的噪声，并且还以毫发无损的方式降低了图形卷积的复杂性。实验表明，LCF提高了图形卷积的有效性和效率，而我们的GCN的表现明显优于现有的GCN。代码可在\ url {https://github.com/wenhui-yu/lcfn}上找到。
\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}.
