Analysis Method of APP User Behavior Based on Convolutional Neural Network
Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 78-85.doi: 10.11896/jsjkx.210700121

• Database & Big Data & Data Science • Previous Articles     Next Articles

Analysis Method of APP User Behavior Based on Convolutional Neural Network

CHEN Yong-quan, JIANG Ying   

  1. Yunnan Key Lab of Computer Technology Application,Kunming 650500,China
    Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China
  • Received:2021-07-12 Revised:2021-12-10 Published:2022-08-02
  • About author:CHEN Yong-quan,born in 1998,postgraduate,is a member of China Computer Federation.His main research interests include software engineering and so on.
    JIANG Ying,born in 1974,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.Her main research interests include software quality assurance and testing,cloud computing,big data analysis and intelligent software engineering.
  • Supported by:
    National Natural Science Foundation of China(62162038,61462049,61063006,60703116),National Key Research and Development Program of China(2018YFB1003904),Key Project of Yunnan Applied Basic Research(2017FA033) and Open Foundation of Yunnan Key Laboratory of Computer Technology Application(2020101).

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Abstract: With the rapid development of mobile Internet,smart terminal has become an indispensable part of people’s daily life and work.In the process of using smart terminal,a large number of APP operation process records will be generated.By analyzing the user’s APP operation process records,the user’s behaviors in the operation process and the user’s behavior pattern can be obtained,which can help developers maintain and improve the APP software.Existing user behavior analysis is biased towards operation analysis and thebehaviors extraction for user’s operation is lacked.An APP user behaviors analysis method based on convolution neural network is proposed.At first,the APP operations are analyzed,and the user operations in the original APP operation record information are extracted.Then the correlation between APP operation and user’s behaviors is mined,and the similarity matrix between APP operations and APP user’s behaviors is constructed.Finally,the behaviors of users will be extracted.Experiments show that this method can extract and identify the user’s behaviors in the records of APP operationprocess effectively,which will be helpful to explore the deep meaning of user’s behaviors.

Key words: APP software, APP user behavior, Convolutional neural network, Operation analysis, Operation process record of user APP

CLC Number: 

  • TP311
[1]AHMED N.Generation Z’s Smartphone and Social MediaUsage:A Survey[J].Journalism and Mass Communication,2019,9(3):101-122.
[2]CAO H,LIN M.Mining smartphone data for app usage prediction and recommendations:A survey[J].Pervasive and Mobile Computing,2017,37:1-22.
[3]ZHAO S,XU F,XU Y,et al.Investigating smartphone userdifferences in their application usage behaviors:an empirical study[J].CCF Transactions on Pervasive Computing and Interaction,2019,1(2):140-161.
[4]SHEN K,YE X J,LIU X N,et al.Android APP behavior-intention inference based on API usage analysis [J].Journal of Tsinghua University(Science and Technology),2017,57(11):1139-1144.
[5]LIU Y Q,CEN R W,ZHANG M,et al.Automatic Search Engine Performance Evaluation Based on User Behavior Analysis[J].Journal of Software,2008,19(11):3023-3032.
[6]KE Y,LIU Y,LIN B Q,et al.Positive and Unlabeled Learning for User Behavior Analysis Based on Mobile Internet Traffic Data[J].IEEE Access,2018,6:37568-37580.
[7]LIAO Z F,LI S J,HE D Y,et al.Analysis of Key User Behavior in GitHub Open Source Software Development[J].Journal of Chinese Computer Systems,2019,40(1):164-168.
[8]LI B.Research and Implementation of APP User Behavior Ana-lysis Based on Android[D].Beijing:Beijing University of Posts and Tele-communications,2018.
[9]ZHANG L,ZHAO N.Internet Users’ Browsing BehaviorsAnalysis[J].Computer Systems & Applications,2016,25(6):260-264.
[10]LI Q,DAN L.Research of Music Recommendation SystemBased on User Behavior Analysis and Word2vec User Emotion Extraction[C]//International Conference on Intelligent & Interactive Systems & Applications.Cham:Springer,2017.
[11]JIANG Z W.AppUsage2Vec:user behavior modeling and application based on mobile app usage record [D].Hangzhou:Zhejiang University,2018.
[12]JIANG Y.Design and implementation of APP recommendation system based on user behavior [D].Nanjing:Southeast University,2018.
[13]CHEN D.Design and implementation of user behavior analysis department based on big data platform [D].Wuhan:Huazhong University of Science & Technology,2016.
[14]GUO J X,GAO C,XU N S,et al.User behavior analysis based on Web browsing logs [J].Computer Science,2014,41(3):110-115.
[15]FUJIO T,TAKAFUMI N,MITSUTERU T,et al.Analysisof User Behavior on Private Chat System[C]//2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops.2016:1-4.
[16]REN Q.Design of Mobile APP User Behavior Analysis Engine Based on Cloud Computing[J/OL].Journal of Physics:Confe-rence Series,2020,1533(2).https://iopscience.iop.org/article/10.1088/1742-6596/1533/2/022092.
[17]WANG R,QIN X,WANG B.Design of Mobile User Behavior Analysis System Based on Big Data[J].China Computer & Communication,2019(11):88-90.
[18]LING Y X,LI R,LAO S Y.Command Space User OperationModel Based on Multi-fingers Touch[J].Computer Enginee-ring,2009,35(10):1-3.
[19]GUO W Y.The Design and Implementation of Personalized Recommender System Based on the Analysis of User’s Behaviors[D].Nanjing:Nanjing University,2012.
[20]SU H,WAN G G.Electronic Forensics System Based on User Behaviors Correlation Analysis[J].Telecommunication Science,2010,26(12):72-78.
[21]LI Y D,HAO Z B,LEI H.Survey of convolutional neural network[J].Journal of Computer Applications,2016,36(9):2508-2515.
[22]LIU X S.Study on Mining Methods and Implementations of Smartphone APP Logs for Understanding User Behaviors[D].Lanzhou:Northwest Minzu University,2020.
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