FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models

FairDiff is a new fairness‑aware diffusion framework designed to mitigate the self‑reinforcing Matthew Effect in Diffusion Recommender Models (DRMs). It introduces Popularity Condition Guidance (PCG) to reweight inference‑time gradients and penalize high‑popularity items, and a Semantic Calibration (SC) module that aligns forward and reverse distributions via optimal transport. Experiments show FairDiff achieves state‑of‑the‑art performance while reducing popularity bias in DRMs.

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• diffusion
• benchmarks
• safety