Contrasting Transformer and Hypergraph Network for Cooperative Sequential Recommendation

Tongyu Wu, Jianfeng Qu, Deqing Wang, Zhiming Cui, Guanfeng Liu, Pengpeng Zhao

Abstract: Recently, transformer has been widely used for sequential recommendation due to its superior sequence modeling and information sensing capabilities. Meanwhile, some studies capture high-order cooperative signals between sequences by graph structure. However, the general graph structure is not enough to capture nonlinear high-order cooperative signals and there are no detailed studies to balance the sequence-level information and the global graph-level higher-order information in sequential recommendation. To solve these challenges, we propose a model called Contrasting Transformer and Hypergraph Network for Cooperative Sequential Recommendation (THCSRec) to coordinate sequence-level information with global graph-level information. Specifically, our model uses a transformer network to capture the information of the sequence itself, and a hypergraph neural network to capture the global graph-level high-order information. Furthermore, the two networks cooperate through a contrastive learning task to maximize mutual information. Finally, the representations of the two networks are aggregated for prediction. In the experiments, we conducted extensive evaluation and ablation studies to verify the effectiveness of THCSRec\(^1\) on three real datasets, which exceeded the existing SOTA performance lines.\(^1\)(Our code is available on https://github.com/Elina-wu/THCSRec)