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Bundle recommendation seeks to recommend a bundle of related items to usersto improve both user experience and the profits of platform. Existing bundlerecommendation models have progressed from capturing only user-bundleinteractions to the modeling of multiple relations among users, bundles anditems. CrossCBR, in particular, incorporates cross-view contrastive learninginto a two-view preference learning framework, significantly improving SOTAperformance. It does, however, have two limitations: 1) the two-viewformulation does not fully exploit all the heterogeneous relations among users,bundles and items; and 2) the "early contrast and late fusion" framework isless effective in capturing user preference and difficult to generalize tomultiple views. In this paper, we present MultiCBR, a novel Multi-viewContrastive learning framework for Bundle Recommendation. First, we devise amulti-view representation learning framework capable of capturing all theuser-bundle, user-item and bundle-item relations, especially better utilizingthe bundle-item affiliations to enhance sparse bundles' representations.Second, we innovatively adopt an "early fusion and late contrast" design thatfirst fuses the multi-view representations before performing self-supervisedcontrastive learning. In comparison to existing approaches, our frameworkreverses the order of fusion and contrast, introducing the followingadvantages: 1)our framework is capable of modeling both cross-view and ego-viewpreferences, allowing us to achieve enhanced user preference modeling; and 2)instead of requiring quadratic number of cross-view contrastive losses, we onlyrequire two self-supervised contrastive losses, resulting in minimal extracosts. Experimental results on three public datasets indicate that our methodoutperforms SOTA methods.

MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation