考虑用户与内容提供者双向增长效应，设计更优推荐策略以提升平台长期健康度。
Policy Design for Two-sided Platforms with Participation Dynamics
- 从控制论与博弈论角度建模双侧平台参与动态
- 发现短期贪婪策略会损害整体社会福祉
- 提出兼顾供给方曝光分布的优化算法，适合平台设计者与政策研究者
在双边平台（如视频流媒体或电商）中，观众与内容提供者存在交互式增长关系：观众受益于提供者数量增加，而提供者则受益于观众数量增长。尽管此类‘群体效应’对平台长期健康发展至关重要，但现有推荐策略通常未考虑参与动态。本文首次系统研究了在群体效应下双边平台的动态行为与推荐策略设计。控制论与博弈论分析揭示，标准的‘短视贪婪’策略不可取，强调需关注供给侧因素（即有效分配各提供者群体的曝光），以通过群体增长提升社会福利。我们还提出一种简单算法，可优化长期社会福利，并在合成数据和真实数据实验中验证其有效性。实验代码已开源。
原文摘要 · Abstract (English)
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.
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