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.
作者简介
Wenjie Cai is a master student in telecommunications and information engineering at Nanjing University of Posts and Telecommunications(NUPT).His main research interests include deep learning,and recommender systems,E-mail:1018010235@njupt.edu.cn;correspondence:Yufeng Wang is currently a professor at the College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,China.He is also the guest researcher with the Advanced Research Center for Human Sciences,Waseda University,Japan.His research interests focus on cyber-physicalsocial systems,crowdsourcing system,algorithmic mechanism design and data science,e-health and e-learning,etc,E-mail:wfwang@njupt.edu.cn;Jianhua Ma received the BS and MS degrees in communication systems from National University of Defense Technology(NUDT),China,in 1982 and 1985,respectively,and the PhD degree in information engineering from Xidian University,China,in 1990.He has joined Hosei University since 2000.He is currently a professor at Digital Media Department in the Faculty of Computer and Information Sciences,Hosei University,Japan.He is a member of IEEE and ACM.He has edited 10 books/proceedings,and published more than 150 academic papers in journals,books,and conference proceedings.His research interest is ubiquitous computing,E-mail:jianhua@hosei.ac.jp;Qun Jin is a full professor at the Networked Information Systems Laboratory,Department of Human Informatics and Cognitive Sciences,and Faculty of Human Sciences,Waseda University,Japan.He has been extensively engaged in research works in the fields of computer science,information systems,and social and human informatics.He seeks to exploit the rich interdependence between theory and practice in his work with interdisciplinary and integrated approaches.His recent research interests include human-centric ubiquitous computing,behavior and cognitive informatics,big data,data quality assurance and sustainable use,personal analytics and individual modeling,intelligence computing,blockchain,cyber security,cyber-enabled applications in healthcare,and computing for well-being.He is a senior member of ACM,IEEE,and Information Processing Society of Japan(IPSJ),E-mail:jin@waseda.jp。
