# Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

arXiv: https://arxiv.org/abs/2008.00104 · HTML: https://mlsanity.com/paper/2008.00104/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 30 of 32)

- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities](https://mlsanity.com/paper/2105.02377/) (2021)
- [Diversified Recommendations for Agents with Adaptive Preferences](https://mlsanity.com/paper/2210.07773/) (2022)
- [pyRDDLGym: From RDDL to Gym Environments](https://mlsanity.com/paper/2211.05939/) (2022)
- [Learning with Exposure Constraints in Recommendation Systems](https://mlsanity.com/paper/2302.01377/) (2023)
- [Individually Fair Ranking](https://mlsanity.com/paper/2103.11023/) (2021)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists](https://mlsanity.com/paper/2404.04269/) (2024)
- [Incentivizing High-Quality Content in Online Recommender Systems](https://mlsanity.com/paper/2306.07479/) (2023)
- [Addressing the Long-term Impact of ML Decisions via Policy Regret](https://mlsanity.com/paper/2106.01325/) (2021)
- [Fast online ranking with fairness of exposure](https://mlsanity.com/paper/2209.13019/) (2022)
- [Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems](https://mlsanity.com/paper/2309.00940/) (2023)
- [Matching of Users and Creators in Two-Sided Markets with Departures](https://mlsanity.com/paper/2401.00313/) (2023)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces](https://mlsanity.com/paper/2306.10606/) (2023)
- [Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies](https://mlsanity.com/paper/2409.17436/) (2024)
- [Algorithmic Fairness: A Tolerance Perspective](https://mlsanity.com/paper/2405.09543/) (2024)
- [Overhead-free User-side Recommender Systems](https://mlsanity.com/paper/2411.07589/) (2024)
- [Policy Design for Two-sided Platforms with Participation Dynamics](https://mlsanity.com/paper/2502.01792/) (2025)
- [Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?](https://mlsanity.com/paper/2502.20783/) (2025)
- [Fair Clustering: Critique, Caveats, and Future Directions](https://mlsanity.com/paper/2406.15960/) (2024)
- [Bandit based centralized matching in two-sided markets for peer to peer lending](https://mlsanity.com/paper/2105.02589/) (2021)
- [PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators](https://mlsanity.com/paper/2403.15524/) (2024)
- [Welfare-Centric Clustering](https://mlsanity.com/paper/2508.10345/) (2025)

## Abstract (arXiv)

Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In realistic settings, this is often not true---the dynamics of an RS ecosystem couple the long-term utility of all agents. In this work, we explore settings in which content providers cannot remain viable unless they receive a certain level of user engagement. We formulate the recommendation problem in this setting as one of equilibrium selection in the induced dynamical system, and show that it can be solved as an optimal constrained matching problem. Our model ensures the system reaches an equilibrium with maximal social welfare supported by a sufficiently diverse set of viable providers. We demonstrate that even in a simple, stylized dynamical RS model, the standard myopic approach to recommendation---always matching a user to the best provider---performs poorly. We develop several scalable techniques to solve the matching problem, and also draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.