Floor It, Then Fill It: Simulating Creator Ecosystems for Fair Video-Content Recommendation Systems at Billion User Scale

Sohini Roychowdhury; Long Sha; Sunand Agarwal; Payal Kamboj; Shuyang Chen; Junlin Zhang; Scott Chen; Hui Zhang

cs.LGOct 28, 2026

Floor It, Then Fill It: Simulating Creator Ecosystems for Fair Video-Content Recommendation Systems at Billion User Scale

Authors: Sohini Roychowdhury, Long Sha, Sunand Agarwal, Payal Kamboj, Shuyang Chen, Junlin Zhang, Scott Chen, Hui Zhang

Organizations: Meta Platforms, Inc., Menlo Park, CA 94025, USA

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)~~creator diversity, modeled via a clustering approach grounded in real platform creator-level distributions; (ii)behavioral fidelity, captured through coupled creator-content state machines that represent growth trajectories, engagement accumulation, and graduation dynamics; and (iii)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.

Figures & tables

Figure 1: Simulation environment for evaluating fair view-budgeting policies. The budgeted MAB allocator operates over weekly decision epochs combining a non-negotiable parity floor (Phase 1) with reward-steered discretionary allocation (Phase 2). Creator lifecycle stages, parity-target definitions, and four reward functions are varied across configurations.

Type	Definition	Interpretation
T1, large under-parity	$g>0.5$	Far below target ( $A<0.5E$ )
T2, small under-parity	$0<g\leq 0.5$	Just below target
T3, small over-parity	$-1\leq g\leq 0$	Just above target
T4, large over-parity	$g<-1$	Far above target ( $A>2E$ )

Table 1: Weekly-Creator type definitions by VPV-parity gap.

Wk	T1	T2	T3	T4	Total
0	11,192	1,104	1,964	12,699	26,959
4	14,799	1,312	2,282	16,459	34,852
8	17,417	1,049	1,589	20,040	40,095
11	18,045	1,019	1,526	21,727	42,317

Table 2: Simulated creators per parity-gap cluster each week.

Metric	Exploration	Growth	Graduation
Active Creators	10,775	8,488	6,148
EWTR (%)	22.93	24.25	30.54
LTR (%)	3.118	2.612	3.472
1d1K Rate (%)	83.33	87.63	92.32
1d10K Rate (%)	27.44	30.01	37.71
1d100K Rate (%)	1.99	1.37	2.18

Table 3: Simulated creator tier performance metrics per week.

Method	Spearman- $\rho$	Funded Jac.	Top-10% Jac.	Turnover
vpv_only	0.520	0.816	0.700	0.190
parity_gap	0.645	0.819	0.729	0.144
parity_x_eng.	0.524	0.819	0.725	0.195
hybrid	0.635	0.817	0.715	0.153

Table 4: Stability comparison across reward methods under optimal configuration.

Figure 2: Sensitivity to the reach weight $w$ in the hybrid reward. (a) Allocation stability: Top-10% Jaccard and Spearman $\rho$ decrease as $w$ increases, while turnover rises. (b) Budget distribution: higher $w$ shifts over-funding from small to large creators.

Starting cluster	Growth ratio	Median VPV: wk0 $\to$ wk11
T1 large-under	170.98 $\times$	$10\to 14$ (breakout tail drives mean)
T2 small-under	23.03 $\times$	$8{,}997\to 30{,}920$
T3 small-over	11.57 $\times$	$14{,}872\to 34{,}104$
T4 large-over	1.46 $\times$	$115{,}809\to 139{,}824$

Table 5: Mean actual-VPV growth ratio (week 11 / week 0).

Figure 3: Week-over-week allocation stability across four reward functions ( $\rho{=}0.7$ , graduated target). (a) Spearman rank correlation; (b) Funded-set Jaccard; (c) Top-10% Jaccard; (d) Turnover (lower is better). The parity-gap reward $r^{\mathrm{gap}}$ dominates on all metrics, with turnover declining to 0.14 by week 11.

Panel of Figure 4 — Figure 4: Simulation-to-production alignment of D-VPV distributions. (a) Weekly allocated D-VPVs per creator cluster. (b) Organic-to-allocated VPV ratio per cluster (log scale). Both panels show consistent week-over-week trends between simulated and production environments.

Figure 5: Simulated creator VPV-trajectories for ten sampled creators across all four tiers. The key takeaway is the compounding effect of sustained VPV-funding on reach and the subsequent transition from boost-dependence to organic self-sustainability.

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Industrial recommenders give new content initial views through budgeted exploration, then use early performance to decide further delivery. On many short-video platforms, exploration is the primary way new videos reach viewers. Viewer-side tests measure consumption; the published budget objectives we review omit creator response. We analyze four experiments on a major short-video platform. An eight-month creator ablation finds production exploration raises videos posted per creator by 8.55% and creators posting at least once by 7.10% relative to a minimal floor. A budget-matched reallocation raises creator participation with no detectable short-run viewer-side change. A year-long viewer ablation finds 1.74% more video views but 2.13% less view time. A delivered view creates immediate feed value, can trigger organic take-up, and can induce creator supply. Take-up and supply replenish a shared corpus, creating two measurement limits. Viewer-side A/B tests cancel the corpus effect when both arms consume the same corpus. Giving each arm its own corpus avoids cancellation, but turnover still controls the horizon. If the corpus turns over at rate w per posting cycle, a t-cycle experiment expresses at most wt of the eventual corpus effect. More users reduce noise but do not speed turnover. Before the corpus path visibly bends, data cannot distinguish a modest fast effect from an arbitrarily large slow one, so a valid confidence interval may lack a finite upper endpoint. As predicted, the three-week co-diverted experiment cannot determine the sign of the eventual corpus effect. Within the window, it identifies the direct feed effect, and an exploratory cohort analysis detects organic lift after exploration ends. The experiments establish a positive creator response, measure the gross corpus flow visible within three weeks, and show the design and duration needed to identify total value.

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