Agile and Accurate CTR Prediction Model Training for Massive-Scale Online Advertising Systems

Abstract

Deep neural network has been adopted as the standard model to predict ads click-through rate (CTR) for commercial online advertising systems. Deploying an industrial scale ads system requires to overcome numerous challenges, e.g., hundreds or thousands of billions of input features and also hundreds of billions of training samples, which under the cost budget can cause fundamental issues on storage, communication, or the model training speed. In this work, we present Baidu's industrial-scale practices on how to apply the system and machine learning techniques to address these issues and increase the revenue. In particular, we focus on the strategy for developing GPU-based CTR models combined with quantization techniques to build a compact and agile system which noticeably improves the revenue. With quantization, we are able to effectively increase the model (embedding layer) size without increasing the storage cost. This brings an increase in prediction accuracy and yields a 1% revenue increase and 1.8% higher relative click-through rate in the real sponsored search production environment.