Adversarial regularized diffusion model for fair recommendations

doi:10.1016/j.neunet.2025.107695

2025-06-15

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DOI：10.1016/j.neunet.2025.107695

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随着推荐系统的广泛部署，人们对推荐结果中的算法公平性和表征偏见日益关注。现有的消偏方法主要存在以下两个关键局限性：（1）显式特征移除策略可能消除与敏感属性纠缠的语义信号，不可避免地降低推荐性能；（2）传统的对抗学习框架通过强制梯度反转来确保与敏感属性无关，但公平目标与推荐目标之间的无控对抗冲突会导致潜在表征的语义失真。

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Neural Networks

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6.3

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8.2K

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