Less Traffic, Better Outcomes: Competition-Aware Request Dispatch in Real-Time Ad Exchanges

AI 中文解读

核心亮点：广告竞价系统不再“广撒网”，而是精准筛选，结果流量降了34%，收入反而涨了4.6%，堪称“少即是多”的典范。通俗解读：想象一下，你开了一家拍卖行，每天有上亿件商品涌进来，但你只有有限的拍卖师。以前你什么都接，结果拍卖师忙不过来，很多好货被耽误了。现在这套系统像个聪明的“分拣员”，它提前预测每件商品能拍出什么价，只把值得拍的送到对应的拍卖师手里。它还每天学习市场变化，自动调整筛选标准，越筛越准。实际影响：这项技术可能让数字广告变得更“安静”。你上网时，背后那些烦人的广告请求会变少，但广告商投放更精准，平台赚得更多。长远看，广告成本可能下降，优质内容平台能获得更稳定收入，而用户看到的广告也可能更贴合需求，减少无效打扰。

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.