Layer-refined Graph Convolutional Networks for Recommendation

Understand

Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological structure of the user-item interaction graph.

• However, these GCN-based recommendation models not only suffer from over-smoothing when stacking too many layers but also bear performance degeneration resulting from the existence of noise in user-item interactions.
• In this paper, we first identify a recommendation dilemma of over-smoothing and solution collapsing in current GCN-based models.
• Specifically, these models usually aggregate all layer embeddings for node updating and achieve their best recommendation performance within a few layers because of over-smoothing.