具有参与动态的双边平台政策设计
Policy Design for Two-sided Platforms with Participation Dynamics
AI总结：
本文首次研究双边平台在群体效应下的参与动态，提出考虑提供者侧曝光分配的政策设计以优化长期社会福利，并验证了算法的有效性。
摘要：
在双边平台（如视频流媒体或电子商务）中，观看者和提供者参与互动动态：观看者受益于提供者群体的增长，而提供者则受益于观看者群体的增长。尽管这种“群体效应”对平台的长期健康至关重要，但推荐政策通常不考虑参与动态。因此，本文首次在群体效应下研究双边平台上的动态和推荐政策设计。我们的控制和博弈论研究结果警告不要使用标准的“短视贪婪”政策，并揭示了提供者侧考虑（即在提供者群体间有效分配曝光）对于通过人口增长改善社会福利的重要性。我们还提出了一种简单的算法，通过考虑群体效应来优化长期社会福利，并在合成数据和真实数据实验中证明了其有效性。我们的实验代码可在 https://github.com/sdean-group/dynamics-two-sided-market 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
