# ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems

arXiv: https://arxiv.org/abs/2509.03661 · HTML: https://mlsanity.com/paper/2509.03661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction](https://mlsanity.com/paper/2408.06512/) (2024)

## Built on by (most cited first, 1 of 1)

- [MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation](https://mlsanity.com/paper/2510.15286/) (2025)

## Abstract (arXiv)

Recommender systems often must maximize a primary objective while ensuring secondary ones satisfy minimum thresholds, or "guardrails." This is critical for maintaining a consistent user experience and platform ecosystem, but enforcing these guardrails despite orthogonal system changes is challenging and often requires manual hyperparameter tuning. We introduce the Automated Constraint Targeting (ACT) framework, which automatically finds the minimal set of hyperparameter changes needed to satisfy these guardrails. ACT uses an offline pairwise evaluation on unbiased data to find solutions and continuously retrains to adapt to system and user behavior changes. We empirically demonstrate its efficacy and describe its deployment in a large-scale production environment.