Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content recommendation platforms. This burgeoning competition reshapes the dynamics of content distribution and profoundly impacts long-term user welfare on the platform. However, the absence of a comprehensive picture of global user preference distribution often traps the competition, especially the creators, in states that yield sub-optimal user welfare. To encourage creators to best serve a broad user population with relevant content, it becomes the platform's responsibility to leverage its information advantage regarding user preference distribution to accurately signal creators. In this study, we perform system-side user welfare optimization under a competitive game setting among content creators. We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content. These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies. To validate the effectiveness of our proposed method, we report our findings from a series of experiments, including: 1. a proof-of-concept negative example illustrating how creators' strategies converge towards sub-optimal states without platform intervention; 2. offline experiments employing our proposed intervention mechanisms on diverse datasets; and 3. results from a three-week online experiment conducted on a leading short-video recommendation platform.

翻译：由创作者经济催生的新型经济机会驱动下，越来越多的内容创作者依赖并通过在线内容推荐平台产生的收入展开竞争。这种日益激烈的竞争重塑了内容分发动态，并对平台的长期用户福利产生深远影响。然而，由于缺乏全局用户偏好分布的完整图景，竞争（尤其是创作者）常陷入产生次优用户福利的状态。为使创作者更好地服务广泛用户群体并提供相关内容，平台需利用其在用户偏好分布方面的信息优势向创作者传递精准信号。本研究在内容创作者间的竞争博弈环境下，从系统侧进行用户福利优化。我们提出了一种平台端算法方案：通过动态计算每个用户基于其推荐内容满意度的权重序列，并基于这些权重设计机制来调整推荐策略或推荐后奖励，从而影响创作者的内容生产策略。为验证所提方法的有效性，我们报告了系列实验结果：1. 概念验证性负面示例，展示了无平台干预时创作者策略如何收敛至次优状态；2. 基于多种数据集的离线实验，采用所提出的干预机制；3. 在某头部短视频推荐平台开展的三周在线实验结果。