Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
Abstract
Diffusion recommender systems achieve strong recommendation accuracy but often suffer from popularity bias, resulting in unequal item exposure. To address this shortcoming, we introduce A2G-DiffRec, a diffusion recommender that incorporates adaptive autoguidance, where the main model is guided by a less-trained version of itself. Instead of using a fixed guidance weight, A2G-DiffRec learns to adaptively weigh the outputs of the main and weak models during training, supervised by a fairness-aware regularization that promotes balanced exposure across items with different popularity levels. Experimental results on three public datasets show that A2G-DiffRec is effective in enhancing item-side fairness at a marginal cost of accuracy reduction compared to existing guided diffusion recommenders and other non-diffusion baselines.
Keywords
Cite
@article{arxiv.2602.14706,
title = {Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems},
author = {Zihan Li and Gustavo Escobedo and Marta Moscati and Oleg Lesota and Markus Schedl},
journal= {arXiv preprint arXiv:2602.14706},
year = {2026}
}
Comments
Accepted at SIGIR 2026