Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems
CoRR(2024)
摘要
Online platforms such as YouTube, Instagram, TikTok heavily rely on
recommender systems to decide what content to show to which users. Content
producers often aim to produce material that is likely to be shown to users and
lead them to engage with said producer. 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 that are interested in maximizing
user engagement. We study two variants of the content-serving rule that the
platform's recommender system uses, and we show structural results on
producers' production at equilibrium. We leverage these structural results to
show that, in simple settings, we see specialization naturally arising 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 show i) the performance and convergence of our heuristic,
ii) the producer and user utilities at equilibrium, and iii) the level of
producer specialization.
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