论文标题
后验概率重要：在线广告中神经预测的双重自适应校准
Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
论文作者
论文摘要
预测用户响应概率对于广告排名和招标至关重要。我们希望预测模型可以产生准确的概率预测，以反映真正的可能性。校准技术旨在对后验概率进行后处理模型预测。场级校准 - 执行校准W.R.T.对于特定的现场价值 - 是细粒度的，更实用的。在本文中，我们提出了一种双重自适应方法阿达卡利布。它学习了一个等渗函数家族，可以通过后统计的指导来校准模型预测，并设计了现场自适应机制，以确保后验适用于要校准的场值。实验验证了阿达卡利布（Adacalib）在校准性能方面取得了重大改善。它已在网上部署并击败了先前的方法。
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.
