Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2026

Abstract

Bundle recommendation seeks to recommend a bundle of related items to users to improve both userexperience 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 learningframework, significantly improving SOTA performance. It does, however, have two limitations: (1) the twoview 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 difficultto generalize to multiple views.In this article, we present MultiCBR, a novel Multi-view Contrastive learning framework for BundleRecommendation. First, we devise a multi-view representation learning framework capable of capturing allthe user-bundle, user-item, and bundle-item relations, especially better utilizing the bundle-item affiliations toenhance 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. Incomparison to existing approaches, our framework reverses the order of fusion and contrast, introducing thefollowing 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 ofcross-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.The code and dataset can be found in the github repo https://github.com/HappyPointer/MultiCBR.

Keywords

Bundle recommendation, graph neural network, contrastive learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Information Systems

Volume

42

Issue

4

First Page

1

Last Page

23

ISSN

1046-8188

Identifier

10.1145/3640810

Publisher

Association for Computing Machinery (ACM)

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