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