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Discovering shilling groups in a real e-commerce platform

Youquan Wang (College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China AND Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China)
Zhiang Wu (Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China)
Zhan Bu (Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China)
Jie Cao (Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China AND College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China)
Dun Yang (School of Information Engineering, Nanjing University of Finance and Economics, Nanjing, China)

Online Information Review

ISSN: 1468-4527

Article publication date: 8 February 2016

1163

Abstract

Purpose

With the popularity of e-commerce, shilling attack is becoming more rampant in online shopping websites. Shilling attackers publish mendacious ratings as well as reviews for promoting or suppressing target products. The purpose of this paper is to investigate group shilling, a new typed shilling attack, behavior in a real e-commerce platform (e.g. Amazon.cn).

Design/methodology/approach

Several behavioral features are proposed for modeling the shilling group, and thus an unsupervised ranking method based on principal component analysis (PCA) is presented for identifying shilling groups from real users on Amazon.cn.

Findings

As indicated by the behavior analysis, the proposed method has successfully identified a number of shilling groups on Amazon. Meanwhile, the effectiveness of the proposed features and accuracy of the proposed unsupervised method are carefully validated.

Originality/value

This paper presents a set of solutions for discovering shilling groups when the ground truth labels are hard to be obtained in real environment, including candidate groups generation, behavioral features definition and unsupervised detection.

Keywords

Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (NSFC) under Grants 71571093, 71372188 and 61502222, National Center for International Joint Research on E-Business Information Processing under Grant 2013B01035, National Key Technologies R & D Program of China under Grant 2013BAH16F03, Industry Projects in Jiangsu S & T Pillar Program under Grant BE2014141, Natural Science Foundation of Jiangsu Province of China under Grant SBK2015042593 and Key Project of Natural Science Research in Jiangsu Provincial Colleges and Universities under Grant 12KJA520001, 14KJA520001, 14KJB520015, 15KJB520012 and 15KJB520011.

Citation

Wang, Y., Wu, Z., Bu, Z., Cao, J. and Yang, D. (2016), "Discovering shilling groups in a real e-commerce platform", Online Information Review, Vol. 40 No. 1, pp. 62-78. https://doi.org/10.1108/OIR-03-2015-0073

Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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