用自适应引导提升扩散推荐系统的物品公平性
Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
- 主模型由弱化版本自适应引导，动态调整权重
- 在三个数据集上显著改善物品曝光公平性，精度损失小
- 适合关注推荐公平性的研究者与工业应用
扩散推荐系统虽具高推荐精度，但常存在流行度偏差，导致物品曝光不均。为此，本文提出A2G-DiffRec，一种引入自适应自引导的扩散推荐模型，其中主模型由自身较弱版本引导。不同于固定引导权重，A2G-DiffRec在训练中学习动态加权主模型与弱模型输出，受公平性感知正则化监督，促进不同流行度物品间均衡曝光。在三个公开数据集上的实验表明，相较于现有引导式扩散推荐模型及其他非扩散基线，A2G-DiffRec能有效提升物品侧公平性，仅以微小精度损失为代价。
原文摘要 · Abstract (English)
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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