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.

本文提出了一种利用高斯图与原型对比学习（GPCL）框架来解决捆绑推荐中的不确定性和采样偏倚问题的方法，并在公共数据集上取得了最新的最佳性能。