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.

翻译：扩散推荐系统虽能实现高精度推荐，但常因流行度偏差导致项目曝光不均衡。针对这一缺陷，我们提出A2G-DiffRec——一种融合自适应自动引导的扩散推荐模型，其核心思想是让主模型接受自身低训练版本的引导。不同于固定引导权重的传统方法，A2G-DiffRec在训练过程中学习自适应地权衡主模型与弱模型的输出，并通过公平性感知正则化约束促进不同流行度项目间的曝光均衡。在三个公开数据集上的实验表明：相比现有引导式扩散推荐模型及其他非扩散基线方法，A2G-DiffRec在极小精度损失代价下有效提升了项目侧公平性。