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

Understand

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.