NAILS不用重训练就能让推荐结果更符合公平、多样性的规范，而且几乎不牺牲用户参与度，很实用。
NAILS是一种简单可扩展的方法，通过内部标签转移将推荐系统输出与物品属性的目标分布对齐。它不需要重训练现有模型，只修改用户条件物品分布来诱导指定的边际分布。实验在多个推荐基准上显示，NAILS能持续改善属性级对齐度，且对用户参与度影响极小。该方法适用于公平性、多样性等规范性目标。
Normative Alignment of Recommender Systems via Internal Label Shift
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.