摘要
随着互联网和广告平台的飞速发展,面对海量的广告信息,为了提升用户点击率,提出一种改进的基于组合结构的逻辑回归点击预测算法LRCS(Logical Regression of Combination Structure)。该算法基于不同类别特征广告受众可能不同的特点,首先,采用FM进行特征组合,产生两类组合特征;其次,将一类特征组合作为聚类算法的输入进行聚类;最后,将另一类特征组合输入由聚类产生的分段GBDT+逻辑回归组合的模型中进行预测。在两个公开数据集中进行了多角度验证,结果表明与其他几类常用的点击预测算法相比,LRCS在点击预测上有一定的性能提升。
With the rapid development of the Internet and advertising platforms,in the face of massive advertising information,in order to improve the user click rate,an improved logical regression click prediction algorithm,logical regression of combination structure(LRCS)based on composite structure is proposed.The algorithm is based on different types of features,which may have different audiences.First,FM is used to combine features to generate two types of combined features.Secondly,a kind of feature combination is used as clustering algorithm for clustering.Finally,another type of feature combination is input into the segmented GBDT+logical regression combination model generated by clustering for prediction.Through multi angle verification in two public datasets,and compared with other commonly used click prediction algorithms,it shows that LRCS has a certain performance improvement in click prediction.
作者
郭尚志
廖晓峰
鲜开义
GUO Shangzhi;LIAO Xiaofeng;XIAN Kaiyi(College of Computer Science,Chongqing University,Chongqing 400030,China)
出处
《计算机科学》
CSCD
北大核心
2024年第2期73-78,共6页
Computer Science
关键词
逻辑回归
特征组合
聚类
组合推荐
人工智能
智能制造
Logical regression
Feature combination
Clustering
Combination recommendation
Artificial intelligence
Intelligent manufacturing
作者简介
通信作者:郭尚志,born in 1982,Ph.D,se-nior engineer.His main research intere-sts include artificial intelligence and intelligent manufacturing,intelligent recom-mendation,machine learning,and big data applications.(921919001@qq.com)。
