摘要
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.
作者
刘冶
刘荻
王砚文
傅自豪
印鉴
LIU Ye;LIU Di;WANG Yanwen;FU Zihao;YIN Jian(School of Data and Computer Science,Sun Yat-sen University,Guangzhou 510006,China;Guangdong Provincial Key Laboratory of Big Data Analysis and Processing,Guangzhou 510006,China;Data Center,Flamingo Network Co.,Ltd.,Guangzhou 510630,China;Department of Computing,The Hong Kong Polytechnic University,Hong Kong 999077,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第1期178-185,191,共9页
Computer Engineering
基金
广东省科技计划项目(2012A010701013)
广州市科技计划项目(2013J4500059)
广州市天河区科技计划项目(201601YG152
201701YG127)
广东省大数据分析与处理重点实验室开放基金(2017017
201805)
关键词
计算广告
广告点击率
特征选择
机器学习
预测模型
computational advertising
advertising Click Through Rate(CTR)
feature selection
machine learning
prediction model
