Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing…

• NN
  National Natural Science Foundation of China

  Awards: 61932009, 2018YFB0804205, 61732008, 61725203, 61602147, 61972125, 61722204
• NK
  National Key Research and Development Program of China

  Awards: 2018YFB0804205, 61732008