Sample Optimization for Display Advertising

Abstract

Sample optimization, which involves sample augmentation and sample refinement, is an essential but often neglected component in modern display advertising platforms. Due to the massive number of ad candidates, industrial ad service usually leverages a multi-layer funnel-shaped structure involving at least two stages: candidate generation and re-ranking. In the candidate generation step, an offline neural network matching model is often trained based on past click/conversion data to obtain the user feature vector and ad feature vector. However, there is a covariate shift problem between the user observed ads and all possible ones. As a result, the candidate generation model trained from the click/conversion history cannot fully capture users' potential intentions or generalize well to unseen ads. In this paper, we utilize several sample optimization strategies to alleviate the covariate shift problem for training candidate generation models. We have launched these strategies in Baidu display ad platform and achieved considerable improvements in offline metrics, including both offline click-recall, cost-recall, as well as online metric cost per mille (CPM).