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Incentivizing High-Quality Content in Online Recommender Systems

Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt

TL;DR

The paper reveals that standard no-regret online learning algorithms in content-recommender Stackelberg setups induce diminishing producer effort and suboptimal user welfare over time. It develops incentive-aware algorithms, including punitive and welfare-focused schemes, that sustain high producer effort and align content with heterogeneous user preferences. The methods leverage a dynamic Stackelberg framework with $D$-dimensional content vectors, contextual linear bandits, and carefully designed punishment criteria to shape equilibrium behavior. The findings highlight a tension between learning efficiency and content quality, offering practical guidance for platform design to improve user welfare while preserving adaptive learning. Overall, the work contributes both negative results for conventional learners and constructive policy tools to mitigate misaligned incentives in online recommender systems.

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.

Incentivizing High-Quality Content in Online Recommender Systems

TL;DR

The paper reveals that standard no-regret online learning algorithms in content-recommender Stackelberg setups induce diminishing producer effort and suboptimal user welfare over time. It develops incentive-aware algorithms, including punitive and welfare-focused schemes, that sustain high producer effort and align content with heterogeneous user preferences. The methods leverage a dynamic Stackelberg framework with -dimensional content vectors, contextual linear bandits, and carefully designed punishment criteria to shape equilibrium behavior. The findings highlight a tension between learning efficiency and content quality, offering practical guidance for platform design to improve user welfare while preserving adaptive learning. Overall, the work contributes both negative results for conventional learners and constructive policy tools to mitigate misaligned incentives in online recommender systems.

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.
Paper Structure (59 sections, 33 theorems, 108 equations, 1 table, 3 algorithms)

This paper contains 59 sections, 33 theorems, 108 equations, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Societal effects of recommender systems.
  4. Learning in Stackelberg games.
  5. Online learning algorithms.
  6. Model
  7. Online learning framework.
  8. Platform algorithms.
  9. Producer incentives.
  10. Performance metrics.
  11. Model Discussion
  12. A stylized online content recommendation marketplace.
  13. A generalized Stackelberg game.
  14. LinHedge/LinEXP3 Induces Low-Quality Content
  15. Upper bounds on producer effort
  16. ...and 44 more sections

Key Result

Theorem 1

Let $\beta \in (0,1)$ be the discount factor of producers, $c \ge 1$ be the cost function exponent, and $\mathcal{D}$ be any distribution over users. Suppose that the platform runs LinHedge with learning rate schedule $\eta_1\ge \eta_2\ge \cdots\ge \eta_T>0$ and $\gamma\in(0,1)$. At any mixed-strate

Theorems & Definitions (55)

  • Theorem 1
  • Theorem 2
  • Lemma 2
  • Theorem 3
  • Theorem 4
  • Corollary 5
  • Theorem 6
  • proof : Proof sketch
  • Proposition 6
  • Proposition 6
  • ...and 45 more