发表机构
Kuaishou Technology（快手科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在线广告中延迟反馈下的长期转化预测问题，提出TWICE框架，将长期点击后转化率分解，利用双时钟提供互补监督训练模型，实验证明该方法有效，提升了快手广告系统的相关指标。
AI 中文摘要
延迟反馈下的长期转化预测在在线广告中产生了双时钟、双窗口学习问题。短的基础观察窗口在点击结果成熟前释放近期点击，而转化在较长的目标转化窗口内持续到达。点击时钟提供及时但部分观察的状态监督，转化时钟揭示长尾延迟。我们提出TWICE框架，将长期点击后转化率分解为目标窗口转化概率和分组经过延迟累积分布函数。两个时钟提供互补监督，通过当前状态似然训练目标窗口CVR头，新到达的转化训练转化时钟上的延迟模型。实验表明TWICE有效，在快手广告系统中提升了预期收入、收入和转化率。
英文摘要
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.
