Bundle recommendation aims to provide a bundle of items to satisfy the user preference on e-commerce platform. Existing successful solutions are based on the contrastive graph learning paradigm where graph neural networks (GNNs) are employed to learn representations from user-level and bundle-level graph views with a contrastive learning module to enhance the cooperative association between different views. Nevertheless, they ignore the uncertainty issue which has a significant impact in real bundle recommendation scenarios due to the lack of discriminative information caused by highly sparsity or diversity. We further suggest that their instancewise contrastive learning fails to distinguish the semantically similar negatives (i.e., sampling bias issue), resulting in performance degradation. In this paper, we propose a novel Gaussian Graph with Prototypical Contrastive Learning (GPCL) framework to overcome these challenges. In particular, GPCL embeds each user/bundle/item as a Gaussian distribution rather than a fixed vector. We further design a prototypical contrastive learning module to capture the contextual information and mitigate the sampling bias issue. Extensive experiments demonstrate that benefiting from the proposed components, we achieve new state-of-the-art performance compared to previous methods on several public datasets. Moreover, GPCL has been deployed on real-world e-commerce platform and achieved substantial improvements.

翻译：捆绑推荐旨在通过提供一组商品来满足电商平台用户的偏好。现有成功方案基于对比图学习范式，该范式利用图神经网络（GNN）从用户级和捆绑级图视图中学习表示，并通过对比学习模块增强不同视图间的协同关联。然而，这些方法忽略了不确定性这一关键问题——由于高度稀疏性或多样性导致缺乏判别性信息，该问题在实际捆绑推荐场景中具有显著影响。我们进一步指出，其逐实例对比学习无法区分语义相似的负样本（即采样偏差问题），导致性能下降。本文提出一种新颖的高斯图与原型对比学习（GPCL）框架以应对这些挑战。具体而言，GPCL将每个用户/捆绑/商品嵌入为高斯分布而非固定向量。我们进一步设计原型对比学习模块来捕获上下文信息并缓解采样偏差问题。大量实验表明，得益于所提出的组件，我们在多个公开数据集上相较于先前方法取得了新的最优性能。此外，GPCL已在真实电商平台部署并取得显著改进。