Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' competition impacts user welfare and how the relevance-driven recommendation influences the dynamics in the long run are still largely unknown. This work provides theoretical insights into these research questions. We model the creators' competition under the assumptions that: 1) the platform employs an innocuous top-$K$ recommendation policy; 2) user decisions follow the Random Utility model; 3) content creators compete for user engagement and, without knowing their utility function in hindsight, apply arbitrary no-regret learning algorithms to update their strategies. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on $K$ and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the myopic approach to the recommendation, i.e., relevance-driven matching performs reasonably well in the long run, as long as users' decisions involve randomness and the platform provides reasonably many alternatives to its users.

翻译：内容创作者在推荐平台上争夺曝光机会，这种策略性行为会导致内容分布的动态变化。然而，创作者之间的竞争如何影响用户福利，以及相关性驱动的推荐在长期内如何影响动态演变，这些问题至今仍不清楚。本文对这些研究问题提供了理论洞见。我们在以下假设条件下对创作者竞争进行建模：1）平台采用无害的Top-$K$推荐策略；2）用户决策遵循随机效用模型；3）内容创作者争夺用户参与度，且在事后不了解其效用函数的情况下，采用任意无遗憾学习算法更新策略。我们通过"无效率代价"的视角研究用户福利保障，证明由创作者竞争导致的用户福利损失比例始终受限于一个仅取决于$K$和用户决策随机性的小常数；同时证明了该上界的紧致性。我们的结果揭示了短视推荐方法的内在优势：只要用户决策存在随机性且平台向用户提供足够多的替代选项，相关性驱动的匹配在长期内就能保持合理性能。