流量更少效果更好：实时广告交易中的竞争感知请求调度
Less Traffic, Better Outcomes: Competition-Aware Request Dispatch in Real-Time Ad Exchanges
RTB请求转发太多？这篇论文用竞争感知调度砍掉34%流量，收入还涨4.6%，搞广告系统的朋友可以读读。
实时竞价（RTB）广告交易平台通常将几乎所有请求转发给需求方平台（DSP），但实际出价率很低。一项新研究提出竞争感知请求调度框架，利用分布性出价预测和概率转发，决定每个请求是否发送给每个DSP。
系统通过轻量策略优化动态调整每个DSP的阈值，适应市场变化。在日请求量超200亿的生产平台上的四轮在线实验中，全多DSP部署使DSP请求量降低34.
2%，净收入提升4.6%（p<0.
001）。
Less Traffic, Better Outcomes: Competition-Aware Request Dispatch in Real-Time Ad Exchanges
Real-time bidding (RTB) ad exchanges typically forward nearly all incoming requests to demand-side platforms (DSPs), even though only a small fraction receive bids. This over-distribution weakens auction outcomes: DSPs throttle participation under compute and budget constraints, reducing the effective use of limited bidding capacity. We present a competition-aware request dispatch framework that uses distributional bid prediction and probabilistic forwarding to decide whether each request should be sent to each DSP. The system adapts per-DSP thresholds over time through lightweight policy optimization to track non-stationary market conditions. We evaluate the framework through four sequential online experiments on a production platform serving over 20 billion daily requests. A full multi-DSP deployment reduces DSP request volume under the policy by 34.2% while increasing net revenue by 4.6% (p<0.001) in a recent 14-day window after an initial DSP adaptation period. Further analysis highlights strong heterogeneity across traffic segments and reveals that aggregate metrics can be misleading. Segment-level and per-DSP analyses suggest that the policy surfaces comparative advantages among DSPs, improving monetized outcomes without increasing overall request volume.
本条情报归属 AITOP「论文」核心追踪栏目。涉及核心实体与技术议题包括：实时竞价、DSP、请求调度、广告系统，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Jonaid Shianifar, Blaz Mramor, Fangda Zou, Matthieu C. Martin, Xingsheng Guo, Zhihua Zhu, Rong Zhou, Bichen Shi），经 AITOP 多维评分管线结构化提炼。