🤖 AI Summary
研究通过推荐算法优化社交媒体平台上的注意力分配，以平衡广告收入与创作者激励，采用两面市场模型分析内容多样性和质量的影响。
📝 Abstract
We study revenue-maximizing attention allocation on an ad-funded social media platform governed by recommendation algorithms. Attention is costly and can be monetized through advertising or allocated to increase creators' exposure, creating a trade-off between monetization and production incentives. In a two-sided model with heterogeneous viewers and creators under private information, the optimal recommendation mix includes content that is ex post suboptimal for some viewers to leverage network externalities. These distortions are targeted: low-ability creators are excluded, while high-ability creators are subsidized through exposure or monetary payments. Two-sided complementarities reshape content variety and quality, with implications for personalization regulation and advertising markets.