发现内容平台在用户满意度与创作者产量间存在根本权衡
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms
- 通过理论与实证揭示低探索策略短期提升满意度但损害内容多样性
- 高探索策略虽略降用户满意度，却显著提升创作者内容产出量
- 提出优化方法可平衡双方利益，适合平台算法设计与审核
在用户生成内容（UGC）平台中，推荐算法显著影响创作者的内容生产动机，因其决定内容获得的用户流量。这一机制微妙地塑造了内容池的规模与多样性，对平台可持续性至关重要。本文通过理论分析与实证研究发现，纯粹以相关性为导向且探索力度弱的策略虽能提升短期用户满意度，却损害内容池的长期丰富性；而更具攻击性的探索策略虽轻微降低用户满意度，却促进更高内容创作量。研究揭示了用户即时满意度与整体内容生产间的根本权衡。基于此，我们提出一种高效优化方法，用于确定最优探索强度，平衡用户与创作者参与度。该模型可作为UGC平台推荐算法部署前的审计工具，帮助对齐短期目标与长期可持续性。
原文摘要 · Abstract (English)
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.
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