DHCL-BR: Dual Hypergraph Contrastive Learning for Bundle Recommendation

Abstract

Abstract As an extension of conventional top-K item recommendation solution, bundle recommendation has aroused increasingly attention. However, because of the extreme sparsity of user-bundle (UB) interactions, the existing top-K item recommendation methods suffer from poor performance when applied to bundle recommendation. While some graph-based approaches have been proposed for bundle recommendation, these approaches primarily leverage the bipartite graph to model the UB interactions, resulting in suboptimal performance. In this paper, a dual hypergraph contrastive learning model is proposed for bundle recommendation. First, we model the direct and indirect UB interactions as hypergraphs to represent the higher-order UB relations. Second, we utilize the hypergraph convolution networks to learn the user and bundle embeddings from the hypergraphs, and improve the learned embeddings through a bidirectional contrastive learning strategy. Finally, we adopt a joint loss that combines the InfoBPR loss supporting multiple negative samples and the contrastive losses to optimize model parameters for prediction. Experiments on the real-world datasets indicate that our model performs better than the state-of-the-art baseline methods.