Leveraging Post-Click User Behaviors for Calibrated Conversion Rate Prediction Under Delayed Feedback in Online Advertising
Yuyao Guo, Xiang Ao, Qiming Liu, Qing He · 2023
Obtaining accurately calibrated conversion rate predictions is essential for the bidding and ranking process in online advertising systems. Nevertheless, the inherent latency between clicks and conversions leads to delayed feedback, which may introduce bias into the prediction models. Compared to indefinitely long conversion delays, post-click user behaviors manifest within a relatively brief time and have been empirically validated to exert a favorable influence on the precision of conversion rate estimates. In light of this, we propose a novel approach that leverages post-click user behaviors to calibrate conversion rate predictions. Specifically, we treat user behaviors as predictable targets to improve accuracy and enhance timeliness. An adaptive loss function based on task uncertainty is employed for multi-task learning. To further reduce calibration error, we integrate the modified prediction model with a parameterized scaling technique. Experiments conducted on two real-world datasets demonstrate that our proposed method outperforms existing models in providing more calibrated predictions.