Platform content interventions in recommendation systems are typically evaluated as static "nudges", ignoring that the systems adaptively learn from the resulting user behavior. We investigate this dynamic through a large-scale field experiment on a short-video platform. The experiment involves a "sleep reminder" campaign designed to reduce late-night usage. Paradoxically, the intervention increased late-night engagement by 14.75% and overall platform usage by 2.18%, and the effects persisted for weeks even after the experiment. We explain this through a forced-exploration mechanism, showing that by revealing high latent demand for the promoted content, the intervention triggers a recommendation policy update that routine user behavior would not produce. The data generated by the intervention induced the algorithm to update its post-campaign policy, reinforcing the very engagement loops the campaign aimed to mitigate. Our findings demonstrate that user-facing interventions can effectively retrain the underlying algorithm, triggering durable, system-wide shifts in content distribution that challenge standard evaluation metrics in platform governance and social responsibility initiatives.

翻译：平台内容干预在推荐系统中通常被评估为静态“助推”，忽略了系统会从由此产生的用户行为中自适应学习。我们通过短视频平台上的一项大规模现场实验来研究这一动态过程。该实验涉及一项旨在减少深夜使用的“睡眠提醒”活动。矛盾的是，该干预措施使深夜参与度增加了14.75%，平台整体使用量增加了2.18%，且这些效应在实验结束后持续数周。我们通过一种强制探索机制解释这一现象，表明通过揭示推广内容的高潜在需求，干预会触发常规用户行为无法产生的推荐策略更新。干预生成的数据促使算法更新其活动后策略，强化了活动本欲缓解的用户参与循环。我们的研究结果表明，面向用户的干预能有效重新训练底层算法，引发内容分发的持久性、系统性转变，这对平台治理与社会责任倡议中的标准评估指标构成挑战。