Improving Dropout in Graph Convolutional Networks for Recommendation via Contrastive Loss

Abstract

We propose a novel graph convolutional network (GCN)-based recommendation model that incorporates a contrastive loss. Although GCN-based recommendation models achieve high recommendation performance, existing models suffer from over-fitting since they explicitly encode the interactions as a graph. To mitigate this problem, while the models perform dropout which just randomly drops edges in the graph, this can miss the essential information that represents users’ preferences. The proposed method improves and extends the dropout via the contrastive loss and enhances the robustness while preserving the essential information. The contrastive loss can correlate the representations in different perturbed graphs and promote their consistency. Conceptually, our method captures users’ preferences which are invariant even in the perturbed interactions. The experimental results demonstrate that our method improves the recommendation performance in real-world datasets.