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 popularity regularization that promotes balanced exposure across items with different popularity levels. Experimental results on the MovieLens-1M, Foursquare-Tokyo, and Music4All-Onion 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.

翻译：扩散推荐系统虽能实现较高的推荐准确率，但常受流行度偏差影响，导致物品曝光不均。为弥补此不足，本文提出A2G-DiffRec——一种融入自适应自动引导机制的扩散推荐模型，其核心思想是让主模型接受其自身欠训练版本的引导。不同于使用固定引导权重的方法，A2G-DiffRec在训练过程中通过监督学习自适应地权衡主模型与弱模型的输出，该监督信号来自一项促进不同流行度物品均衡曝光的流行度正则化项。在MovieLens-1M、Foursquare-Tokyo和Music4All-Onion数据集上的实验结果表明，相较于现有引导扩散推荐模型及其他非扩散基线方法，A2G-DiffRec能以极小的精度损失为代价，有效提升物品侧公平性。