Computer Science ›› 2022, Vol. 49 ›› Issue (7): 170-178.doi: 10.11896/jsjkx.210600092

• Artificial Intelligence • Previous Articles     Next Articles

Method for Abnormal Users Detection Oriented to E-commerce Network

DU Hang-yuan1, LI Duo1, WANG Wen-jian1,2   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory of Computational Intelligence and Chinese Information Processing(Shanxi University),Ministry of Education,Taiyuan 030006,China
  • Received:2021-06-10 Revised:2021-10-25 Online:2022-07-15 Published:2022-07-12
  • About author:DU Hang-yuan,born in 1985,Ph.D,associate professor,master supervisor.His main research interests include cluster analysis and complex network theory.
    WANG Wen-jian,born in 1968,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include machine learning,data mining and computational intelligence.
  • Supported by:
    National Natural Science Foundation of China(61902227,62076154,U1805263),Special Foundation from the Central Finance to Support the Development of Local University(YDZX20201400001224),Natural Science Foundation of Shanxi Province,China(201901D211192) and Science Foundation of the Higher Education Institutions of Shanxi Province,China(2019L0039).

Abstract: In the e-commerce network,abnormal users often show different behavioral characteristics from normal users.Detecting abnormal users and analyzing their behavior patterns is of great practical significance to maintaining the order of e-commerce platforms.By analyzing the behavior patterns of abnormal users,we abstract the e-commerce network into the heterogeneous information network,and convert it into a user-device bipartite graph.On this basis,we propose a method for detecting abnormal users oriented to e-commerce network——self-supervised anomaly detection model(S-SADM).The model has a self-supervised learning mechanism.It uses an autoencoder to encode the user-device bipartite graph to obtain user node representations.By optimizing the joint objective function,the model completes backpropagation,and uses support vector data descriptions to perform anomaly detection on user node representations.After the automatic iterative optimization of the network,the user node representation has supervised information,and we obtain relatively stable detection results.Finally,S-SADM is validated on 3 real network datasets and a semi-synthetic network dataset,and the experimental results demonstrate the effectiveness and superiority of the method.

Key words: Anomaly detection, Autoenco-der, E-commerce network, Heterogeneous information network, Self-supervised learning, Support vector data description

CLC Number: 

  • TP183
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