# From Recommendation Systems to Facility Location Games

arXiv: https://arxiv.org/abs/1809.02931 · HTML: https://mlsanity.com/paper/1809.02931/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems](https://mlsanity.com/paper/2309.00940/) (2023)
- [PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators](https://mlsanity.com/paper/2403.15524/) (2024)
- [Content Creation with Spillovers: An Incentive Design Approach](https://mlsanity.com/paper/2603.14372/) (2026)

## Abstract (arXiv)

Recommendation systems are extremely popular tools for matching users and contents. However, when content providers are strategic, the basic principle of matching users to the closest content, where both users and contents are modeled as points in some semantic space, may yield low social welfare. This is due to the fact that content providers are strategic and optimize their offered content to be recommended to as many users as possible. Motivated by modern applications, we propose the widely studied framework of facility location games to study recommendation systems with strategic content providers. Our conceptual contribution is the introduction of a textitmediator to facility location models, in the pursuit of better social welfare. We aim at designing mediators that a) induce a game with high social welfare in equilibrium, and b) intervene as little as possible. In service of the latter, we introduce the notion of textitintervention cost, which quantifies how much damage a mediator may cause to the social welfare when an off-equilibrium profile is adopted. As a case study in high-welfare low-intervention mediator design, we consider the one-dimensional segment as the user domain. We propose a mediator that implements the socially optimal strategy profile as the unique equilibrium profile, and show a tight bound on its intervention cost. Ultimately, we consider some extensions, and highlight open questions for the general agenda.