计算机应用与软件2018，Vol.35Issue(1)：107-111,148,6.DOI:10.3969/j.issn.1000-386x.2018.01.018
FM集成模型在广告点击率预估中的应用
THE APPLICATION OF INTEGRATION MODEL BASED ON FACTORIZATION MACHINE IN ADVERTISING CTR PREDICTION
摘要
Abstract
The current ad click rate estimation model has limited leaming ability for sparse,unequal distribution of ad data.To solve this problem,this paper proposed an Integrated Model of Factorization Machine based on the respective data sampling to predict advertising CTR.The Gradient Boost Decision Tree Algorithm was used to extract the high-level features as the input features of the Factorization Machine to combine automatically,to find the correlation between the features,and to solve the problem of sparse data and imbalanced classification.In this paper,Hadoop was used to train the fusion model of Gradient Boost Decision Tree Algorithm + Factorization Machine in parallel to reduce the time cost.Through the single model experiment,model contrast experiment,the sampling experiment and the model integration experiment,the optimum sampling proportion was determined,and the validity of Integration Model based on Factorization Machine was verified.关键词
CTR预估/FM集成模型/Hadoop大数据平台/互联网广告Key words
CTR prediction/Integration model of factorization machine/Hadoop/Internet advertising分类
信息技术与安全科学引用本文复制引用
潘博,张青川,于重重,谢小兰..FM集成模型在广告点击率预估中的应用[J].计算机应用与软件,2018,35(1):107-111,148,6.基金项目
北京市自然科学基金重点项目(KZ201410011014) （KZ201410011014）
北京市教委科研计划面上项目(KM201510011009) （KM201510011009）
北京市教委科研计划面上项目(KM201510011010). （KM201510011010）
