Disentangled Contrastive Bundle Recommendation with Conditional Diffusion

Abstract

Bundle recommendation aims to improve user experience by suggesting complementary items that users are likely to purchase together. Although recent advances in recommendation systems have shown promise, there are still significant challenges: i) The dynamic nature of user preferences and interactions introduces noise that can distort the effectiveness of recommendations. ii) Existing methods frequently exhibit limited robustness when addressing the sparsity of user interactions with bundles in real-world scenarios. To tackle these issues, we introduce a disentangled contrastive bundle recommendation (DCBR) framework with conditional diffusion. First, we propose a conditional bundle diffusion model for denoising the user-bundle interaction graph, introducing a bundle latent consistency constraint during the optimization process to mitigate the degradation of original interaction information. Subsequently, we design a triple-view denoised graph learning module to obtain effective representations from multiple views. Furthermore, we present a dual-level disentangled contrastive learning paradigm, which addresses the latent relationships at two levels: between views (inter-view) and within each view (intra-view). By maximizing the consistency between positive samples in these contrastive views, we generate disentangled contrastive signals, overcoming interaction sparsity and alleviating noise issues. Our experimental evaluations on three benchmark datasets reveal that DCBR significantly outperforms state-of-the-art methods.