دورية أكاديمية
Method for Abnormal Users Detection Oriented to E-commerce Network
| العنوان: | Method for Abnormal Users Detection Oriented to E-commerce Network |
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| المؤلفون: | DU Hang-yuan, LI Duo, WANG Wen-jian |
| المصدر: | Jisuanji kexue, Vol 49, Iss 7, Pp 170-178 (2022) |
| بيانات النشر: | Editorial office of Computer Science, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer software LCC:Technology (General) |
| مصطلحات موضوعية: | anomaly detection, e-commerce network, heterogeneous information network, self-supervised learning, autoenco-der, support vector data description, Computer software, QA76.75-76.765, Technology (General), T1-995 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | Chinese |
| تدمد: | 1002-137X |
| Relation: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-7-170.pdf; https://doaj.org/toc/1002-137X |
| DOI: | 10.11896/jsjkx.210600092 |
| URL الوصول: | https://doaj.org/article/7f34ebf2780840309e1a200bf81ab669 |
| رقم الأكسشن: | edsdoj.7f34ebf2780840309e1a200bf81ab669 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 1002137X |
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| DOI: | 10.11896/jsjkx.210600092 |