解决广告点击后长期转化预测中的延迟反馈问题。
TWICE: Two-Clock, Two-Window Learning for Long-Horizon Conversion Prediction in Online Advertising
- 用双时钟机制分离点击与转化信号，分别建模转化概率和延迟分布。
- 在真实广告系统中提升收入2.486%、转化量2.061%，效果显著。
- 无需历史查表或卷积，部署简单，适合大规模工业应用。
在线广告中，因转化反馈延迟，长周期转化预测面临双时钟、双窗口的学习挑战。短观测窗口在点击发生后及时释放数据，但转化结果尚未成熟；而转化事件则在整个目标转化窗口内持续到达。点击时钟提供及时但不完整状态监督，转化时钟揭示长尾延迟，但每个到达时间片内的延迟分布受历史点击群体影响，这些群体具有不同流量规模和转化率。本文提出TWICE框架，将长周期点击后转化率（CVR）分解为目标窗口转化概率与分组累计延迟分布函数（CDF）。两个时钟提供互补监督：点击记录通过基线观测窗口的当前状态似然训练目标窗口CVR头；新到达的转化数据在转化时钟上训练延迟模型。为处理群体混合问题，TWICE采用固定点击时间预测的CVR（pCVR）质量作为群体暴露，在到达条件似然下建模。由此生成的聚合记录自包含，单一学习得到的CDF可对所有请求的预测时长（至目标窗口）产生单调预测。推理无需历史查找或卷积操作。在公开基准与工业广告数据集上的实验表明，该方法有效。在快手广告系统线上A/B测试中，相比对照组，预期收入提升2.486%，收入提升1.858%，转化量提升2.061%。该方法已部署至全量流量。
原文摘要 · Abstract (English)
Long-horizon conversion prediction under delayed feedback creates a two-clock, two-window learning problem in online advertising. A short base observation window releases recent clicks on the click clock before their outcomes mature, whereas conversions continue to arrive on the conversion clock throughout a longer target conversion window. The click clock provides timely but partially observed status supervision. The conversion clock reveals long-tail delays, but the delay composition within an arrival-time slice is weighted by historical click cohorts with different traffic volumes and target-window conversion rates. We present TWICE, a framework that factorizes long-horizon post-click conversion rate (CVR) into a target-window conversion probability and a grouped elapsed-delay cumulative distribution function (CDF). The two clocks provide complementary supervision. Click-clock records train the target-window CVR head through a current-status likelihood over the base observation window. Newly arrived conversions train the delay model on the conversion clock. To account for the cohort mixture, TWICE uses fixed click-time predicted CVR (pCVR) mass as cohort exposure in an arrival-conditioned likelihood. This accounts for differences in cohort traffic and conversion propensity. The resulting aggregate records are self-contained. A single learned CDF produces monotone predictions for all requested horizons up to the target conversion window. Serving requires neither historical lookup nor convolution. Experiments on a public benchmark and an industrial advertising dataset demonstrate the effectiveness of TWICE. In an online A/B test in Kwai's advertising system, TWICE increased expected revenue, revenue, and conversions by 2.486%, 1.858%, and 2.061%, respectively. It was subsequently deployed to full traffic.
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