Incentivizing High-Quality Content in Online Recommender Systems

Understand

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.