{"ID":6536295,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10915","arxiv_id":"2607.10915","title":"Normative Alignment of Recommender Systems via Internal Label Shift","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.","short_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 norm...","url_abs":"https://arxiv.org/abs/2607.10915","url_pdf":"https://arxiv.org/pdf/2607.10915v1","authors":"[\"Johannes Kruse\",\"Kasper Lindskow\",\"Michael Riis Andersen\",\"Ryotaro Shimizu\",\"Julian McAuley\",\"Pierre-Alexandre Mattei\",\"Jes Frellsen\"]","published":"2026-07-12T20:43:22Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.LG\"]","methods":"[]","has_code":false}