Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation

Recent years have witnessed a phenomenal success of agent-initiated social e-commerce models, which encourage users to become selling agents to promote items through their social connections. The complex interactions in this type of social e-commerce can be formulated as \emph{Heterogeneous Information Networks} (HIN), where there are numerous types of relations between three types of nodes, i.e., users, selling agents and items. Learning high quality node embeddings is of key interest, and \emph{Graph Convolutional Networks} (GCNs) have recently been established as the latest state-of-the-art methods in representation learning. However, prior GCN models have fundamental limitations in both modeling heterogeneous relations and efficiently sampling relevant receptive field from vast neighborhood. To address these problems, we propose \emph{RecoGCN}, which stands for a RElation-aware CO-attentive GCN model,  to effectively aggregate heterogeneous features in a HIN. It makes up current GCN’s limitation in modelling heterogeneous relations with a relation-aware aggregator, and leverages the semantic-aware meta-paths to carve out concise and relevant receptive fields for each node. To effectively fuse the embeddings learned from different meta-paths, we further develop a co-attentive mechanism to dynamically assign importance weights to different meta-paths by attending the three-way interactions among users, selling agents and items. Extensive experiments on a real-world dataset demonstrate RecoGCN is able to learn meaningful node embeddings in HIN, and consistently outperforms baseline methods in recommendation tasks.