On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated user traffic. This phenomenon subtly shapes the volume and diversity of the content pool, which is crucial for the platform's sustainability. In this work, we demonstrate, both theoretically and empirically, that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. In contrast, a more aggressive exploration policy may slightly compromise user satisfaction but promote higher content creation volume. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on UGC platforms. Building on this finding, we propose an efficient optimization method to identify the optimal exploration strength, balancing user and creator engagement. Our model can serve as a pre-deployment audit tool for recommendation algorithms on UGC platforms, helping to align their immediate objectives with sustainable, long-term goals.

翻译：在用户生成内容（UGC）平台上，推荐算法通过竞争性地分配用户流量，显著影响创作者的内容生产动机。这一现象微妙地塑造了内容池的规模与多样性，这对平台的可持续发展至关重要。本文从理论与实证两方面证明，一种探索强度较低、纯粹以相关性为导向的推荐策略，虽能提升短期用户满意度，却会损害内容池的长期丰富性。相比之下，更具探索性的推荐策略可能会轻微影响用户满意度，但能促进更高的内容创作量。我们的研究揭示了UGC平台上即时用户满意度与整体内容生产之间的根本性权衡。基于这一发现，我们提出了一种高效的优化方法，以确定最佳的探索强度，从而平衡用户与创作者的参与度。该模型可作为UGC平台推荐算法在部署前的审计工具，帮助将其即时目标与可持续的长期目标相协调。