申请试用
HOT
登录
注册
 
Improving Ad Click Prediction by Considering Non-displayed Events

Improving Ad Click Prediction by Considering Non-displayed Events

陈重丶
/
发布于
/
938
人观看

Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classi!cation problems, where displayed events with and without click feedbacks are respectively considered as positive and negative instances for training and of-“ine validation. However, due to the selection mechanism applied in most advertising systems, a selection bias exists between distributions of displayed and non-displayed events. Conventional CTR models ignoring the bias may have inaccurate predictions and cause a loss of the revenue. To alleviate the bias, we need to conduct counterfactual learning by considering not only displayed events but also non-displayed events. In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system. To overcome these difficulties, we propose a novel framework for counterfactual CTR prediction. In experiments, we compare our proposed framework against state-of-the-art conventional CTR models and existing counterfactual learning approaches.

0 点赞
0 收藏
1下载
相关文档
确认
3秒后跳转登录页面
去登陆