典型文献
CAN:Effective Cross Features by Global Attention Mechanism and Neural Network for Ad Click Prediction
文献摘要：
Online advertising click-through rate (CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction.Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors.However,real-world data present a complex and nonlinear structure.Hence,second-order feature interactions are unable to represent cross information adequately.This drawback has been addressed using deep neural networks (DNNs),which enable high-order nonlinear feature interactions.However,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features.In this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN models.The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions.The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.
文献关键词：
中图分类号：
作者姓名：
Wenjie Cai;Yufeng Wang;Jianhua Ma;Qun Jin
作者机构：
Nanjing University of Posts and Telecommunications (NJUPT),Nanjing,210003,China;Digital Media Department in the Faculty of Computer and Information Sciences,Hosei University,Tokyo 163-8001,Japan;Networked Information Systems Laboratory,Department of Human Informatics and Cognitive Sciences,Waseda University,Tokyo 163-8001,Japan
文献出处：
引用格式：
[1]Wenjie Cai;Yufeng Wang;Jianhua Ma;Qun Jin-.CAN:Effective Cross Features by Global Attention Mechanism and Neural Network for Ad Click Prediction)[J].清华大学学报自然科学版（英文版）,2022(01):186-195
A类：
clicking
B类：
CAN,Effective,Cross,Features,by,Global,Attention,Mechanism,Neural,Network,Ad,Click,Prediction,Online,advertising,through,CTR,prediction,aimed,predicting,probability,user,has,undergone,considerable,development,recent,years,One,hot,topics,this,area,construction,interactions,facilitate,accurate,Factorization,machine,provides,second,order,linearly,multiplying,hidden,factors,However,real,world,complex,nonlinear,Hence,unable,represent,cross,information,adequately,This,drawback,been,addressed,using,deep,neural,networks,DNNs,which,enable,high,cannot,easily,optimize,structures,because,absence,original,features,In,study,effective,algorithm,called,explicitly,exploits,benefits,attention,mechanisms,models,used,rich,expressive,low,optimization,predictors,that,implicitly,incorporate,experiments,datasets,demonstrate,our,proposed,performs,better,than,other
AB值：
0.584859
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
