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arXiv · 2607.10915

Normative Alignment of Recommender Systems via Internal Label Shift

Johannes Kruse·Kasper Lindskow·Michael Riis Andersen·Ryotaro Shimizu·Julian McAuley·Pierre-Alexandre Mattei·Jes Frellsen
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Abstract

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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Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Ryotaro Shimizu, Julian McAuley, Pierre-Alexandre Mattei, Jes Frellsen. 2026-07-12. Normative Alignment of Recommender Systems via Internal Label Shift. https://doi.org/10.1145/3705328.3759309

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