Guaranteed display (GD) advertising is a critical component of advertising since it provides publishers with stable revenue and enables advertisers to target specific audiences with guaranteed impressions. However, smooth pacing control for online ad delivery presents a challenge due to significant budget disparities, user arrival distribution drift, and dynamic change between supply and demand. This paper presents robust risk-constrained pacing (RCPacing) that utilizes Lagrangian dual multipliers to fine-tune probabilistic throttling through monotonic mapping functions within the percentile space of impression performance distribution. RCPacing combines distribution drift resilience and compatibility with guaranteed allocation mechanism, enabling us to provide near-optimal online services. We also show that RCPacing achieves $O(\sqrt{T})$ dynamic regret where $T$ is the length of the horizon. RCPacing's effectiveness is validated through offline evaluations and online A/B testing conducted on Taobao brand advertising platform.

翻译：保量展示广告是广告领域的关键组成部分，因为它为发布商提供稳定的收入，并使广告主能够针对特定受众进行有保障的曝光投放。然而，由于预算差异显著、用户到达分布漂移以及供需动态变化，在线广告投放的平滑节奏控制面临挑战。本文提出了鲁棒风险约束节奏控制（RCPacing），该方法利用拉格朗日对偶乘子，通过印象表现分布分位数空间中的单调映射函数来精细调整概率性节流。RCPacing融合了分布漂移鲁棒性和与保量分配机制的兼容性，能够提供接近最优的在线服务。我们还证明，RCPacing实现了$O(\sqrt{T})$的动态遗憾，其中$T$为时间范围长度。通过在淘宝品牌广告平台上进行的离线评估和在线A/B测试，验证了RCPacing的有效性。