On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated traffic. This phenomenon subtly shapes the volume and diversity of the content pool, which is crucial for the sustainability of the platform. 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. This reveals an intrinsic trade-off between user engagement and content diversity 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 serves as a pre-deployment audit tool for UGC platforms, facilitating the alignment between their short-term and long-term goals.