Incentivizing High-Quality Content in Online Recommender Systems

arXiv:2306.07479v3 Announce Type: replace-cross
Abstract: 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 …

abstract algorithm analyze arxiv cs.gt cs.ir cs.lg equilibrium future game incentives online learning platform platforms quality recommendation recommendation algorithm recommendations recommender systems replace stat.ml study systems tiktok today type youtube