Conversion rate (CVR) prediction is one of the core components in online
recommender systems, and various approaches have been proposed to obtain
accurate and well-calibrated CVR estimation. However, we observe that a
well-trained CVR prediction model often performs sub-optimally during sales
promotions. This can be largely ascribed to the problem of the data
distribution shift, in which the conventional methods no longer work. To this
end, we seek to develop alternative modeling techniques for CVR prediction.
Observing similar purchase patterns across different promotions, we propose
reusing the historical promotion data to capture the promotional conversion
patterns. Herein, we propose a novel \textbf{H}istorical \textbf{D}ata
\textbf{R}euse (\textbf{HDR}) approach that first retrieves historically
similar promotion data and then fine-tunes the CVR prediction model with the
acquired data for better adaptation to the promotion mode. HDR consists of
three components: an automated data retrieval module that seeks similar data
from historical promotions, a distribution shift correction module that
re-weights the retrieved data for better aligning with the target promotion,
and a TransBlock module that quickly fine-tunes the original model for better
adaptation to the promotion mode. Experiments conducted with real-world data
demonstrate the effectiveness of HDR, as it improves both ranking and
calibration metrics to a large extent. HDR has also been deployed on the
display advertising system in Alibaba, bringing a lift of $9\%$ RPM and $16\%$
CVR during Double 11 Sales in 2022.

本研究提出了在销售促销过程中使用历史促销数据进行精准 CVR 预测的历史数据重用 (HDR) 方法。HDR 方法通过数据检索模块、分布偏移校正模块和 TransBlock 模块实现。实验结果表明，HDR 方法可以显著提高排名和校准度指标，此方法已在阿里巴巴的展示广告系统中部署，并在 2022 年的双 11 促销中提高了 %9 的 RPM 和 %16 的 CVR。