Diffusion Contrastive Learning Model for Multi-Behavior Recommendation

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Abstract
Multi-behavior recommendation leverages diverse user interactions to capture personalized user preferences. To alleviate the data sparsity of the crucial target behavior, multi-behavior contrastive learning (MBCL) improves performance by enriching self-supervised signals from auxiliary behaviors. However, existing methods rely on heuristic augmentation techniques to construct contrastive views, which disrupt essential multi-behavior collaborative information; simultaneously, auxiliary behaviors, influenced by multiple confounders, easily introduce semantic confusion into the MBCL alignment process. Inspired by recent advances of diffusion models, we propose DiffMB, a novel diffusion contrastive learning framework. DiffMB first employs a Multi-Behavior Weight-Enhanced Graph Neural Network to capture behavioral heterogeneity through parallel encoding. At the core of DiffMB is a Hybrid Contrastive Learning (HCL) strategy that tackles this dilemma via two synergistic components. First, Target-Behavior Diffusion Contrastive Learning leverages a diffusion process to generate high-quality, semantically consistent views for the target behavior, thereby consolidating its semantics while avoiding the disruption of collaborative information. Second, Multi-Behavior Collaborative Contrastive Learning extracts valuable collaborative signals from auxiliary behaviors. Furthermore, a Dynamic Optimization Strategy is introduced to reconcile gradient conflicts in multi-task training. Extensive experiments on three datasets demonstrate that DiffMB significantly outperforms state-of-the-art baselines.
Funding Information
  • National Natural Science Foundation of China (92046026)

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