Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles and items. CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference learning framework, significantly improving SOTA performance. It does, however, have two limitations: 1) the two-view formulation does not fully exploit all the heterogeneous relations among users, bundles and items; and 2) the "early contrast and late fusion" framework is less effective in capturing user preference and difficult to generalize to multiple views. In this paper, we present MultiCBR, a novel Multi-view Contrastive learning framework for Bundle Recommendation. First, we devise a multi-view representation learning framework capable of capturing all the user-bundle, user-item and bundle-item relations, especially better utilizing the bundle-item affiliations to enhance sparse bundles' representations. Second, we innovatively adopt an "early fusion and late contrast" design that first fuses the multi-view representations before performing self-supervised contrastive learning. In comparison to existing approaches, our framework reverses the order of fusion and contrast, introducing the following advantages: 1)our framework is capable of modeling both cross-view and ego-view preferences, allowing us to achieve enhanced user preference modeling; and 2) instead of requiring quadratic number of cross-view contrastive losses, we only require two self-supervised contrastive losses, resulting in minimal extra costs. Experimental results on three public datasets indicate that our method outperforms SOTA methods.

翻译：[translated abstract in Chinese] 捆绑推荐旨在向用户推荐一组关联物品，以提升用户体验和平台收益。现有捆绑推荐模型已从仅捕捉用户-捆绑交互，发展到对用户、捆绑与物品间多重关系的建模。特别是CrossCBR将跨视角对比学习引入双视角偏好学习框架，显著提升了当前最先进方法的性能。然而该方法存在两个局限：1）双视角结构未能充分利用用户、捆绑与物品间的所有异质关系；2）“先对比后融合”框架在捕捉用户偏好方面效率较低且难以推广至多视角场景。本文提出MultiCBR——一种面向捆绑推荐的新型多视角对比学习框架。首先，我们设计了一个能捕捉用户-捆绑、用户-物品及捆绑-物品关系的多视角表示学习框架，尤其通过更好利用捆绑-物品从属关系增强稀疏捆绑的表示；其次，我们创新性地采用“先融合后对比”设计，先融合多视角表示再执行自监督对比学习。与现有方法相比，本框架反转了融合与对比的顺序，带来以下优势：1）能同时建模跨视角与自我视角偏好，实现更优的用户偏好建模；2）仅需两个自监督对比损失函数，无需二次数量级的跨视角对比损失，从而最小化额外计算成本。在三个公开数据集上的实验结果表明，本方法优于当前最先进方法。