# MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation

arXiv: https://arxiv.org/abs/2311.16751 · HTML: https://mlsanity.com/paper/2311.16751/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2023.

## Built on by (most cited first, 6 of 6)

- [Fine-tuning Multimodal Large Language Models for Product Bundling](https://mlsanity.com/paper/2407.11712/) (2024)
- [Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation](https://mlsanity.com/paper/2505.14901/) (2025)
- [Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?](https://mlsanity.com/paper/2504.17220/) (2025)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [Producer-Fairness in Sequential Bundle Recommendation](https://mlsanity.com/paper/2506.20329/) (2025)
- [Explaining Synergistic Effects in Social Recommendations](https://mlsanity.com/paper/2601.18151/) (2026)

## Abstract (arXiv)

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.