Bundle recommendations strive to offer users a set of items as a package named bundle, enhancing convenience and contributing to the seller's revenue. While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles. This compromise can result in information loss, ultimately impacting the overall model performance. To address this gap, we develop a unified model for bundle recommendation, termed hypergraph-enhanced dual convolutional neural network (HED). Our approach is characterized by two key aspects. Firstly, we construct a complete hypergraph to capture interaction dynamics among users, items, and bundles. Secondly, we incorporate U-B interaction information to enhance the information representation derived from users and bundle embedding vectors. Extensive experimental results on the Youshu and Netease datasets have demonstrated that HED surpasses state-of-the-art baselines, proving its effectiveness. In addition, various ablation studies and sensitivity analyses revealed the working mechanism and proved our effectiveness. Codes and datasets are available at https://github.com/AAI-Lab/HED

翻译：捆绑推荐致力于为用户提供一组物品作为捆绑包，以提升便利性并促进卖家收入。尽管先前的方法已展现出显著性能，我们认为它们可能损害用户、物品与捆绑包之间的三元关系。这种损害可能导致信息损失，最终影响模型的整体性能。为弥补这一不足，我们开发了一个用于捆绑推荐的统一模型，称为超图增强的双卷积神经网络（HED）。我们的方法具有两个关键特征。首先，我们构建了一个完整的超图以捕捉用户、物品与捆绑包之间的交互动态。其次，我们整合了用户-捆绑包交互信息，以增强从用户和捆绑包嵌入向量中提取的信息表示。在Youshu和Netease数据集上的大量实验结果证明，HED超越了现有最先进的基线方法，验证了其有效性。此外，多项消融研究和敏感性分析揭示了其工作机制，并证实了我们的有效性。代码和数据集可在 https://github.com/AAI-Lab/HED 获取。