Summary
Popular video platforms often show videos from creators who already have a lot of viewers, making it hard for new creators to get noticed. The authors created a detailed computer simulation of how many different creators and viewers interact over time. Using this simulation, they tested methods to share views more fairly among creators, especially helping smaller creators grow. Their approach helped increase the reach of smaller creators consistently and kept the recommendations stable over time. They also studied how closely their simulations match real-world results to understand when these fairness ideas can be trusted.
What this means in practice
- •For video platform engineers: Design and test recommendation systems that improve view fairness to help emerging creators get more visibility without sacrificing overall engagement.
- •For online marketplace operators: Use ecosystem simulation to evaluate fair allocation policies for products or sellers in recommendation systems supporting diverse participants.
Authors
Sohini Roychowdhury, Long Sha, Sunand Agarwal, Payal Kamboj, Shuyang Chen, Junlin Zhang, Scott Chen, Hui Zhang
Abstract
Engagement-optimized content recommendation systems exhibit a well-documented rich-get-richer dynamic that structurally disadvantages emerging creators on large-scale platforms. Evaluating debiasing interventions at scale demands faithful simulation of creator-viewer ecosystems, yet the fidelity, diversity, and validity of such simulators remain underexplored. We present a grounded user-simulation framework that models a heterogeneous population of ${\sim}42$K creators and their coupled viewer interactions over 12~weeks, and use it to evaluate equitable view-allocation policies formulated as a budgeted multi-armed bandit (MAB). The simulator captures three behavioral dimensions: (i)~\textbf{creator diversity}, modeled via a clustering approach grounded in real platform creator-level distributions; (ii)~\textbf{behavioral fidelity}, captured through coupled creator-content state machines that represent growth trajectories, engagement accumulation, and graduation dynamics; and (iii)~\textbf{population-level validity}, measured by calibration against observed platform metrics. We evaluate simulative-configurations that vary content-viewership target definitions, reward functions, and a small-creator reserve lever to maximize creator-content recommendation equity. A floor-protected per content-view-target with a view-parity-gap reward guarantees 90\% of view targets being met by creators every week, grows small-creator organic reach ${\sim}{+}33\%$ per week, and achieves high distribution stability (set of creators with funded-views Jaccard $= 0.819$). We analyze the Sim2Real gap and propose validation protocols, contributing evidence for when simulation-derived fairness conclusions can be trusted.