ME-GCN: Motif-Enhanced Graph Convolutional Network for Recommendation Systems

Recommendation systems play a key role in helping users cope with the vast amount of online information, and graph convolutional network GCN -based methods have attracted much attention due to their ability to model complex relationships in user-item interaction graphs. However, existing GCN models mainly focus on direct connections between nodes, ignoring the potential value of higher-order structural patterns such as triangles. In this paper, we propose a motif-enhanced graph convolutional network ME-GCN to improve recommendation performance by explicitly leveraging the triangle patterns in the user-item interaction graph. Specifically, we design an efficient sparse matrix algorithm to compute the triangle participation of nodes and integrate it into the node embedding via a learnable projection mechanism, which enhances the motif capability of higher-order structural patterns while retaining the simple architecture of GCN. Experiments on three public datasets MovieLens-1M, Amazon-Books, and Yelp2018 show that ME-GCN significantly outperforms existing benchmark models, especially in sparse data scenarios up to 7.47 . Ablation experiments further verify the importance of the triangular model, whose contribution far exceeds simple structural features such as the first-order node degree.