Multi-view denoising contrastive learning for bundle recommendation

Abstract

The goal of bundle recommendation is to offer users a set of items that match their preferences. Current methods mainly categorize user preferences into bundle and item levels, and then use graph neural networks to obtain representations of users and bundles at both levels. However, real-world interaction data often contains irrelevant and uninformative noise connections, leading to inaccurate representations of user interests and bundle content. In this paper, we introduce a Multi-view Denoising Contrastive Learning approach for Bundle Recommendation (MDCLBR), aiming to reduce the negative effects of noisy data on users’ and bundles’ representations. We use the original view, which includes bundle and item levels, to guide data augmentation for creating augmented views. Then, we apply the multi-view contrastive learning paradigm to enhance collaboration within the original view, the augmented views, and between them. This leads to more accurate representations of users and bundles, reducing the impact of noisy data. Our method outperforms previous approaches in extensive experiments on three real-world public datasets.