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arXiv · 2602.14706

Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems

Zihan Li·Gustavo Escobedo·Marta Moscati·Oleg Lesota·Markus Schedl
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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.

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Zihan Li, Gustavo Escobedo, Marta Moscati, Oleg Lesota, Markus Schedl. 2026-04-24. Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems. https://doi.org/10.1145/3805712.3809912

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