让推荐系统更符合公平、多样等社会价值，不牺牲用户偏好。
Normative Alignment of Recommender Systems via Internal Label Shift
- 在推荐系统内部调整标签分布，实现属性对齐。
- 提升属性分布匹配度，用户点击率下降不足1%。
- 无需重训练模型，适合实际部署的伦理推荐场景。
我们提出NAILS（基于内部标签转移的推荐系统规范对齐），一种简单且可扩展的方法，用于将推荐结果与项目属性（如类别）的目标分布对齐。仅以用户参与度优化的推荐系统常无法满足公平性、多样性及编辑价值等更广泛规范目标。NAILS通过修改用户条件下的项目分布，在不改变已有推荐系统学习到的用户偏好且无需重新训练模型的前提下，实现特定属性的边缘分布对齐。该问题被形式化为在层次分类框架内应用的标签转移。从利益相关者视角出发，NAILS使推荐输出能够与全局规范目标保持一致。实证表明，NAILS在几乎不影响用户参与度的情况下，持续提升属性层面的对齐效果，为价值驱动的推荐提供了实用机制。
原文摘要 · Abstract (English)
We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions over item-level attributes, such as categories. Recommender systems optimized solely for user engagement often fail to satisfy broader normative objectives, including fairness, diversity, and editorial values. NAILS modifies the user-conditional item distribution to induce a specified marginal distribution over attributes while preserving the preferences learned by an existing recommender system and requiring no model retraining. We formulate this problem as a form of label shift applied internally within a hierarchical classification framework. By adopting a stakeholder-centric perspective, NAILS enables recommendation outputs to be aligned with global normative objectives. Empirically, we show that NAILS consistently improves attribute-level alignment with minimal impact on user engagement, providing a practical mechanism for value-driven recommendation.
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