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：在用户生成内容平台上，推荐算法通过竞争算法分配的用户流量，显著影响创作者生产内容的动机。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。在本研究中，我们从理论和实证两方面证明，一个纯粹以相关性驱动且探索强度较低的政策，虽然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，一个更具侵略性的探索政策可能会轻微影响用户满意度，但能促进更高的内容创作量。我们的研究结果揭示了用户生成内容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，以确定最优的探索强度，从而平衡用户与创作者的参与度。我们的模型可作为用户生成内容平台上推荐算法的部署前审计工具，帮助将其即时目标与可持续的长期目标对齐。