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

2026-09-29

AI Summary

This study addresses the self-reinforcing popularity bias and Matthew effect in diffusion-based recommender models, which stem from structural prior mismatch. To mitigate this issue, we propose a plug-and-play fairness-aware generative framework that operates without modifying the training objective. Specifically, our method reshapes the gradient field through popularity-conditioned guidance, a semantic calibration module, and one-step optimal transport. Furthermore, inference-time reweighting and distribution alignment mechanisms are integrated to break the long-tail suppression cycle. As the primary contribution, this work is the first to reveal the mechanism of prior mismatch in diffusion models and to establish a retraining-free debiasing paradigm. The proposed approach effectively alleviates the Matthew effect while preserving state-of-the-art recommendation performance.

Abstract

While the "Matthew Effect" and filter bubbles are widely recognized outcome-level biases in recommender systems, we reveal that Diffusion Recommender Models (DRMs) uniquely compound this issue through their generative dynamics. Rather than merely inheriting data imbalances, DRMs trigger a self-reinforcing amplification of popularity bias. We identify that this phenomenon is driven by two compounding mechanisms. First, while optimization loss is universally dominated by high-frequency items across recommenders, DRMs suffer from a unique structural prior mismatch during generation. Because the forward terminal distribution of long-tailed data deviates significantly from the standard Gaussian prior, reverse sampling trajectories inherently collapse toward high-density popular items, fundamentally suppressing niche item generation. To dismantle this self-reinforcing loop, we propose FairDiff, a plug-and-play fairness-aware diffusion framework. To overcome the popularity-dominated loss, we introduce Popularity Condition Guidance (PCG). Rather than altering the training objective, PCG acts as an inference-time distributional reweighting mechanism, mathematically reshaping the score-based gradient field to penalize high-popularity regions and guide trajectories toward niche semantics. Furthermore, we design a Semantic Calibration (SC) Module to bridge the prior mismatch, aligning the forward and reverse distributions via one-step optimal transport. Comprehensive evaluations demonstrate that FairDiff achieves state-of-the-art performance while effectively mitigating the self-reinforcing Matthew Effect, highlighting its value as a general framework for DRMs.

Problem

• Diffusion Recommender Models
• Matthew Effect
• Popularity Bias
• Prior Mismatch
• Fairness

Innovation

• Diffusion Recommender Models
• Matthew Effect
• Popularity Condition Guidance
• Semantic Calibration
• Optimal Transport