The phenomenal success of the newly-emerging social e-commerce has demonstrated that utilizing social relations is becoming a promising approach to promote e-commerce platforms. In this new scenario, one of the most important problems is to predict the value of a community formed by closely connected users in social networks due to its tremendous business value. However, few works have addressed this problem because of 1) its novel setting and 2) its challenging nature that the structure of a community has complex effects on its value. To bridge this gap, we develop a Multi-scale Structure-aware Community value prediction network (MSC) that jointly models the structural information of different scales, including peer relations, community structure, and inter-community connections, to predict the value of given communities. Specifically, we first proposed a Masked Edge Learning Graph Convolutional Network (MEL-GCN) based on a novel masked propagation mechanism to model peer influence. Then, we design a Pair-wise Community Pooling (PCPool) module to capture critical community structures. Finally, we model the inter-community connections by distinguishing intra-community edges from inter-community edges and employing a Multi-aggregator Framework (MAF). Extensive experiments on a large-scale real-world social e-commerce dataset demonstrate our method’s superior performance over state-of-the-art baselines, with a relative performance gain of 11.40%, 10.01%, and 10.97% in MAE, RMSE, and NRMSE, respectively. Further ablation study shows the effectiveness of our designed components. Our code and dataset are available1.

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 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 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 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.