Multi-Aspect Heterogeneous Graph Convolutional Network for Recommendation

Graph convolutional networks (GCN), aiming to learn meaningful representations for graph data, has been popularly used in recommender systems since user-item interactions can be represented by a bipartite graph. However, GCN often suffers from the over-smoothing issue when it goes deeper, which implies that long paths between users and items are not very useful to embedding learning in recommender systems. To deal with long paths, introducing user and item links is a promising way to shorten the average path between users and items. However, due to introducing the user and item linkages, the bipartite graph becomes a heterogeneous graph. To this end, we propose a multi-aspect heterogeneous GCN model, which is to power GCN towards recommendation tasks by introducing user-user and item-item links to build the heterogeneous user- item graph. Then, we decompose it into three subgraphs and keep the global heterogeneous graph to preserve the original structure information. Specifically, we apply GCN to learn user and item representations from both the subgraphs and the global graph. After that, we have multi-aspect attention to integrate the representations from GCN to obtain the final user and item representation. Extensive experiments on three real-world datasets show that the proposed approaches achieve significant improvements over state-of-the-art methods.