基于多源融合特征提取的在线广告预测模型

Online Advertising Prediction Model Based on Multiple Source Fusion Feature Extraction

针对智能移动终端应用平台上的广告点击率(CTR)预测问题,在传统PC端Web平台在线广告CTR预测方法的基础上,提出一个新的智能移动终端在线广告投放业务架构。基于此架构,构建基于机器学习的在线广告预测模型,对用户基本信息、广告内容、用户使用环境等多源特征进行融合提取,实现在线广告CTR的精确预测。结合移动APP应用环境的特点,将用户历史行为数据加入预测模型进一步提高CTR预测性能。实验结果表明,该模型具有较高的CTR预测准确率。

Aiming at the problem of advertising Click Through Rate(CTR)prediction on intelligent mobile devices application platform,this paper proposes a novel online advertising business architecture for intelligent mobile devices based on the traditional CTR prediction method on PC Web platform.With this architecture,an online advertising prediction model based on machine learning is designed to integrate and extract the multiple source features such as user information,advertising content and user usage environment,so as to achieve accurate prediction of online advertising CTR.Combined with the characteristics of the mobile APP application environment,the CTR prediction performance is improved by adding the user’s historical behavior data into the prediction model.Experimental results show that this model has a high accuracy rate of CTR prediction.