# Optimizing Rankings for Recommendation in Matching Markets

arXiv: https://arxiv.org/abs/2106.01941 · HTML: https://mlsanity.com/paper/2106.01941/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2021.

## Built on by (most cited first, 12 of 12)

- [Optimizing generalized Gini indices for fairness in rankings](https://mlsanity.com/paper/2204.06521/) (2022)
- [Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking](https://mlsanity.com/paper/2206.07247/) (2022)
- [Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings](https://mlsanity.com/paper/2202.03237/) (2022)
- [Fairness in Matching under Uncertainty](https://mlsanity.com/paper/2302.03810/) (2023)
- [Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method](https://mlsanity.com/paper/2408.09748/) (2024)
- [Off-Policy Evaluation and Learning for Matching Markets](https://mlsanity.com/paper/2507.13608/) (2025)
- [Optimal Analysis for Bandit Learning in Matching Markets with Serial Dictatorship](https://mlsanity.com/paper/2512.06758/) (2025)
- [Learning to Re-rank with Constrained Meta-Optimal Transport](https://mlsanity.com/paper/2305.00319/) (2023)
- [An IPW-based Unbiased Ranking Metric in Two-sided Markets](https://mlsanity.com/paper/2307.10204/) (2023)
- [Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings](https://mlsanity.com/paper/2402.14305/) (2024)
- [Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching](https://mlsanity.com/paper/2602.15752/) (2026)
- [Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation](https://mlsanity.com/paper/2604.21264/) (2026)

## Abstract (arXiv)

Based on the success of recommender systems in e-commerce, there is growing interest in their use in matching markets (e.g., labor). While this holds potential for improving market fluidity and fairness, we show in this paper that naively applying existing recommender systems to matching markets is sub-optimal. Considering the standard process where candidates apply and then get evaluated by employers, we present a new recommendation framework to model this interaction mechanism and propose efficient algorithms for computing personalized rankings in this setting. We show that the optimal rankings need to not only account for the potentially divergent preferences of candidates and employers, but they also need to account for capacity constraints. This makes conventional ranking systems that merely rank by some local score (e.g., one-sided or reciprocal relevance) highly sub-optimal -- not only for an individual user, but also for societal goals (e.g., low unemployment). To address this shortcoming, we propose the first method for jointly optimizing the rankings for all candidates in the market to explicitly maximize social welfare. In addition to the theoretical derivation, we evaluate the method both on simulated environments and on data from a real-world networking-recommendation system that we built and fielded at a large computer science conference.