Hiroki Okamura

Improving Dropout in Graph Convolutional Networks for Recommendation via Contrastive Loss

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. T…