Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems

2024-01-30

arXiv.org

AI Summary

This paper investigates strategic content production behavior under algorithmic recommendation systems (e.g., YouTube, Instagram), where producers optimize for user engagement. Method: We formally characterize the structure of pure-strategy Nash equilibria under recommendation constraints and propose a computationally tractable heuristic algorithm for equilibrium computation, validated via simulation. Contribution/Results: We prove that “spontaneous specialization”—where producers concentrate on distinct embedding-space regions to enhance visibility and interaction—is an inevitable outcome of competitive engagement optimization. Our analysis quantifies specialization intensity and reveals the inherent trade-off between producer revenue and user utility induced by recommendation rules. We further derive design principles that jointly optimize platform objectives (e.g., engagement, diversity) and ecosystem health (e.g., content variety, creator sustainability), offering actionable guidance for equitable and efficient recommender system design.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Algorithm Configuration

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Online platforms such as YouTube, Instagram heavily rely on recommender systems to decide what content to present to users. Producers, in turn, often create content that is likely to be recommended to users and have users engage with it. To do so, producers try to align their content with the preferences of their targeted user base. In this work, we explore the equilibrium behavior of producers who are interested in maximizing user engagement. We study two variants of the content-serving rule for the platform's recommender system, and provide a structural characterization of producer behavior at equilibrium: namely, each producer chooses to focus on a single embedded feature. We further show that specialization, defined as different producers optimizing for distinct types of content, naturally emerges from the competition among producers trying to maximize user engagement. We provide a heuristic for computing equilibria of our engagement game, and evaluate it experimentally. We highlight i) the performance and convergence of our heuristic, ii) the degree of producer specialization, and iii) the impact of the content-serving rule on producer and user utilities at equilibrium and provide guidance on how to set the content-serving rule.

Problem

• Explore producer equilibrium in engagement-driven recommender systems
• Study content-serving rules' impact on producer behavior
• Analyze specialization among producers maximizing user engagement

Innovation

• Recommender system optimization
• Producer specialization analysis
• Equilibrium behavior characterization