Incentivizing High-Quality Content in Online Recommender Systems

TL;DR

This paper examines how online learning algorithms in recommender systems influence content quality, revealing that standard algorithms incentivize low effort, and proposes new algorithms to promote higher-quality content and user welfare.

Contribution

The paper identifies the negative incentives of standard online learning algorithms on content quality and introduces new algorithms that encourage high-effort content creation.

Findings

Standard algorithms like Hedge and EXP3 lead to low effort in content creation.

New algorithms can incentivize producers to invest high effort.

Improved algorithms enhance user welfare by promoting high-quality content.

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 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.