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：捆绑推荐旨在为用户提供一组作为“捆绑包”的物品，从而提高便利性并增加卖方的收益。尽管现有方法已展现出显著性能，但我们认为它们可能损害用户、物品和捆绑包之间的三元关系。这种损害可能导致信息丢失，最终影响整体模型性能。为解决此问题，我们提出了一种统一的捆绑推荐模型，称为超图增强的双重卷积神经网络（HED）。我们的方法具有两个关键特点：首先，我们构建了一个完整的超图来捕捉用户、物品和捆绑包之间的交互动态；其次，我们融入了U-B（用户-捆绑包）交互信息，以增强从用户和捆绑包嵌入向量中派生的信息表示。在Youshu和Netease数据集上的大量实验结果表明，HED超越了现有的最优基线模型，证明了其有效性。此外，多项消融研究和敏感性分析揭示了其工作机制，并进一步验证了本模型的效果。代码和数据集可在https://github.com/AAI-Lab/HED获取。