  In content recommender systems such as TikTok and YouTube, the platform's
recommendation algorithm shapes content producer incentives. Many platforms
employ online learning, which generates intertemporal incentives, since content
produced today affects recommendations of future content. We study the game
between producers and analyze the content created at equilibrium. We show that
standard online learning algorithms, such as Hedge and EXP3, unfortunately
incentivize producers to create low-quality content, where producers' effort
approaches zero in the long run for typical learning rate schedules. Motivated
by this negative result, we design learning algorithms that incentivize
producers to invest high effort and achieve high user welfare. At a conceptual
level, our work illustrates the unintended impact that a platform's learning
algorithm can have on content quality and introduces algorithmic approaches to
mitigating these effects.
