Associated with the Linz Institute of Technology and the Institute of Computational Perception @ JKU Linz, Austria

Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems

sigir_a2g Teaser

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


Citation

Zihan Li, Gustavo Escobedo, Oleg Lesota, Marta Moscati, Markus Schedl
Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2026.

BibTeX

@inproceedings{Li2026sigir_a2g,
    title = {Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems},
    author = {Li, Zihan and Escobedo, Gustavo and Lesota, Oleg and Moscati, Marta and Schedl, Markus},
    booktitle = {Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://arxiv.org/pdf/2602.14706},
    year = {2026},
    year = {2026}
}