mlsanityData to 14 Oct 2026

Policy Design for Two-sided Platforms with Participation Dynamics

arXiv 2502.01792 ↗First posted 3 Feb 2025cs.GT cs.IR cs.LGcited by 2

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Abstract (arXiv)

In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics: viewers benefit from increases in provider populations, while providers benefit from increases in viewer population. Despite the importance of such "population effects" on long-term platform health, recommendation policies do not generally take the participation dynamics into account. This paper thus studies the dynamics and recommender policy design on two-sided platforms under the population effects for the first time. Our control- and game-theoretic findings warn against the use of the standard "myopic-greedy" policy and shed light on the importance of provider-side considerations (i.e., effectively distributing exposure among provider groups) to improve social welfare via population growth. We also present a simple algorithm to optimize long-term social welfare by taking the population effects into account, and demonstrate its effectiveness in synthetic and real-data experiments. Our experiment code is available at https://github.com/sdean-group/dynamics-two-sided-market.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

22 of 36 references.

References · 22

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10 more
  1. 2023How Bad is Top-K Recommendation under Competing Content Creators?cited by 27
  2. 2020User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Marketscited by 23
  3. 2023Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?cited by 10
  4. 2024User Welfare Optimization in Recommender Systems with Competing Content Creatorscited by 10
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  10. 2023Matching of Users and Creators in Two-Sided Markets with Departurescited by 5

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Citing papers per year: 2025: 2.

2 papers.