Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration techniques aim to post-process model predictions to posterior probabilities. Field-level calibration -- which performs calibration w.r.t. to a specific field value -- is fine-grained and more practical. In this paper we propose a doubly-adaptive approach AdaCalib. It learns an isotonic function family to calibrate model predictions with the guidance of posterior statistics, and field-adaptive mechanisms are designed to ensure that the posterior is appropriate for the field value to be calibrated. Experiments verify that AdaCalib achieves significant improvement on calibration performance. It has been deployed online and beats previous approach.

翻译：预测用户响应概率对于广告排序和竞价至关重要。我们希望预测模型能够产生反映真实可能性的准确概率预测。校准技术旨在对模型预测进行后处理，使其逼近后验概率。字段级校准——针对特定字段值执行校准——是一种细粒度且更具实用性的方法。本文提出一种双重自适应方法AdaCalib。该方法学习一个保序函数族，在后验统计量的指导下校准模型预测，并设计了字段自适应机制以确保后验概率适用于待校准的字段值。实验验证表明，AdaCalib在校准性能上取得了显著提升。该方法已在线部署并超越了先前方案。