# Normative Alignment of Recommender Systems via Internal Label Shift

arXiv: https://arxiv.org/abs/2607.10915 · HTML: https://mlsanity.com/paper/2607.10915/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance](https://mlsanity.com/paper/2304.11127/) (2023)
- [A Unified View of Label Shift Estimation](https://mlsanity.com/paper/2003.07554/) (2020)
- [Measuring the Business Value of Recommender Systems](https://mlsanity.com/paper/1908.08328/) (2019)
- [What are you optimizing for? Aligning Recommender Systems with Human Values](https://mlsanity.com/paper/2107.10939/) (2021)
- [A Review of Modern Fashion Recommender Systems](https://mlsanity.com/paper/2202.02757/) (2022)
- [Recommenders with a mission: assessing diversity in newsrecommendations](https://mlsanity.com/paper/2012.10185/) (2020)
- [News Recommendation with Candidate-aware User Modeling](https://mlsanity.com/paper/2204.04726/) (2022)
- [EB-NeRD: A Large-Scale Dataset for News Recommendation](https://mlsanity.com/paper/2410.03432/) (2024)
- [Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation](https://mlsanity.com/paper/2004.09980/) (2020)
- [RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://mlsanity.com/paper/2209.13520/) (2022)
- [Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation](https://mlsanity.com/paper/2307.16089/) (2023)
- [A Fashion Item Recommendation Model in Hyperbolic Space](https://mlsanity.com/paper/2409.02599/) (2024)
- [RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations](https://mlsanity.com/paper/2409.20483/) (2024)
- [Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation](https://mlsanity.com/paper/2410.13248/) (2024)
- [DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation](https://mlsanity.com/paper/2204.10796/) (2022)
- [Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation](https://mlsanity.com/paper/2408.02156/) (2024)

## Abstract (arXiv)

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.