42K创作者仿真框架：让小创作者每周自然曝光增长33%的推荐策略
Floor It, Then Fill It: Simulating Creator Ecosystems for Fair Video-Content Recommendation Systems at Billion User Scale
想搞推荐系统公平性的朋友看这篇：4.2万创作者的仿真环境，一套策略让小创作者周曝光涨33%。
论文针对推荐系统富者愈富的机制提出一个用户仿真框架，模拟约4.2万名创作者在12周内与观众的耦合互动。该框架从创作者多样性、行为保真度、总体有效性三个维度建模，并与真实平台指标做校准。作者将公平的曝光分配策略建模为预算约束的多臂老虎机问题，实验显示带下限保护的目标分配加view-parity-gap奖励函数能保证每周90%的曝光目标达成，小创作者自然触达每周增长约33%，曝光分布稳定性Jaccard达0.819。论文还分析了Sim2Real差距并提出验证协议，说明仿真结论何时可信。
Floor It, Then Fill It: Simulating Creator Ecosystems for Fair Video-Content Recommendation Systems at Billion User Scale
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.
