关键词: 团购推荐, 社交网络, 超图, 双通道, 卷积网络

Abstract: In social e-commerce group buying, how to fully tap the potential preferences of users and participants is an important factor affecting the success of group buying. Most of the existing social recommendation models use pair-wise relationships to mine users’ potential preferences. However, considering the complex interaction between users and participants, it is necessary to establish a high-order relationship between users in the group buying scenario. Therefore, we propose a dual-channel Hypergraph Convolutional network for Group-buying Recommendation, HCGR, and set up different message passing mechanisms in the channel according to different roles in group buying, making full use of high-order social relations to capture users’ potential preferences more comprehensively. Then, the gating mechanism is used to adaptively aggregate the user embedding information of different channels, and finally generate the recommendation results. Experiments on real group buying datasets show that HCGR is superior to all comparison models, and the overall performance gets improved by 3.18%～4.46%. Ablation experiments and data sparsity experiments further verify the rationality of the proposed model.