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New Technology of Library and Information Service  2015, Vol. 31 Issue (11): 41-50    DOI: 10.11925/infotech.1003-3513.2015.11.07
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Collusive Sales Fraud Detection Based on Users' Information Search Behavior Template and Statistical Analysis
Wang Zhongqun, Le Yuan, Xiu Yu, Huang Subin, Wang Qiansong
School of Management Engineering, Anhui Polytechnic University, Wuhu 241000, China
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[Objective] Aiming at collusive sales inflation fraud in e-commerce promotion, this paper presents a collusive product sales fraud detection method based on users' information search behavior.[Methods] Firstly, in order to describe users' information search behavior in online shopping, a model for user information search behavior with keywords and a similarity calculating method for users' information search behavior are proposed. Secondly, a suspicious fraud mining algorithm based on hierarchical clustering algorithm for inflation sales is proposed, which depends on the similarity between users' information search behavior. Finally, this paper proposes a method for detecting suspicious fraud based on statistical analysis, to identify inflating sales in sale record of illegal vendors.[Results] The experimental results show that the recall and precision of the method are 88.6% and 90.1% respectively based on the improved data set.[Limitations] The threshold value predetermined for judging whether the fraudulent behavior is “scalping” behavior is fixed.[Conclusions] The method is effective for the detection of collusive sales inflation fraud based on users' information search behavior template.

Received: 04 June 2015      Published: 06 April 2016
:  G202  

Cite this article:

Wang Zhongqun, Le Yuan, Xiu Yu, Huang Subin, Wang Qiansong. Collusive Sales Fraud Detection Based on Users' Information Search Behavior Template and Statistical Analysis. New Technology of Library and Information Service, 2015, 31(11): 41-50.

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[1] 中国电子商务研究中心. 2012年(上)中国电子商务用户体验与投诉监测报告[EB/OL]. [2013-06-28]. http://www. (China Electronic Commerce Research Center. 2012 (a) Chinese E-commerce User Experience and Complaints Monitoring Reports [EB/OL]. [2013-06-28]. http://www. down/yhty.pdf.)
[2] 威客–猪八戒网.需求和任务推广[EB/OL]. [2014-07-26]. (Wik-Zhubajie Website. Needs and Tasks Promotion [EB/OL]. [2014-07-26]. http://www. wdtg/. )
[3] 王烁, 徐健, 刘颖. 网络“水军”探测方法研究[J]. 现代图书情报技术, 2014(7-8): 92-100. (Wang Shuo, Xu Jian, Liu Ying. Research on Online “Water Army” Detection Methods [J]. New Technology of Library and Information Service, 2014(7-8): 92-100.)
[4] Chen C C, Tseng Y. Quality Evaluation of Product Reviews Using an Information Quality Framework [J]. Decision Support Systems, 2011, 50(4): 755-768.
[5] 李霄, 丁晟春. 垃圾商品评论信息的识别研究[J].现代图书情报技术, 2013(1): 63-68. (Li Xiao, Ding Shengchun. Research on Review Spam Recognition [J]. New Technology of Library and Information Service, 2013(1): 63-68.)
[6] 伍之昂, 王有权, 曹杰. 推荐系统托攻击模型与检测技术[J]. 科学通报, 2014, 59(7): 551-560. (Wu Zhiang, Wang Youquan, Cao Jie. A Survey on Attack Models and Detection Techniques for Recommender Systems [J]. Chinese Science Bulletin, 2014, 59(7): 551-560.)
[7] You W J, Liu L, Xia M, Lv C G. Reputation Inflation Detection in a Chinese C2C Market [J]. Electronic Commerce Research and Applications, 2011, 10(5): 510-519.
[8] Chang W. H, Chang J S. An Effective Early Fraud Detection Method for Online Auction [J]. Electronic Commerce Research and Applications, 2012, 11(4): 346-360.
[9] Lian Q, Zhang Z, Yang Mei, et al. An Empirical Study of Collusion Behavior in the Maze P2P File Sharing System [C]. In: Proceedings of the 27th International Conference on Distributed Computing Systems. 2007.
[10] Uyar M, Yildirim S, Gencoglu M T. An Expert System Based on S-transform and Neural Network for Automatic Classification of Power Quality Disturbances [J]. Expert Systems with Applications, 2009, 36: 5962-5975.
[11] 郑华, 吴克文, 朱庆华. 基于神经网络和SNA的C2C电子商务信誉欺诈识别研究[J]. 计算机应用研究, 2011, 28(5): 1883-1885. (Zheng Hua, Wu Kewen, Zhu Qinghua. Detection of C2C Reputation Fraud Activities Based on Neural Network and SNA [J]. Application Research of Computers, 2011, 28(5): 1883-1885.)
[12] 尤薇佳, 刘鲁, 杨俊杰, 等. 基于交易记录的欺诈识别[C]. 见: 第二届网商及电子商务生态学术研讨会论文集. 杭州: 浙江大学出版社, 2009: 178-182. (You Weijia, Liu Lu, Yang Junjie, et al. Fraud Detection Based on Transaction Records [C]. In: Proceedings of the Second Netrepreneurs and E-business Ecosystem Seminar. Hangzhou: Zhejiang University Press, 2009: 178-182.)
[13] Wang J, Chiu C. Detecting Online Auction Inflated-Reputation Behaviors Using Social Network Analysis [C]. In: Poceedings of the 2005 North American Association for Computational Social and Organizational Science.2005.
[14] Zhu H S, Xiong H, Ge Y, et al. Discovery of Ranking Fraud for Mobile Apps [J]. Transactions on Knowledge and Data Engineering, 2015, 27(1): 74-87.
[15] 王实, 高文, 李锦涛, 等. 路径聚类: 在Web站点中的知识发现[J]. 计算机研究与发展, 2001, 38(4): 482-486. (Wang Shi, Gao Wen, Li Jintao, et al. Path Clustering: Discover the Knowledge in the Website [J]. Journal of Computer Research and Development, 2001, 38(4): 482-486.)
[16] 业宁, 李威, 梁作鹏, 等. 一种Web用户行为聚类算法[J]. 小型微型计算机系统, 2004, 25(7): 1364-1367. (Ye Ning, Li Wei, Liang Zuopeng, et al. Web User Action Clustering Algorithm [J]. Journal of Chinese Computer Systems, 2004, 25(7): 1364-1367.)
[17] 陈敏, 苗夺谦, 段其国. 基于用户浏览行为聚类Web用户[J]. 计算机科学, 2008, 35(3): 186-187. (Chen Min, Miao Duoqian, Duan Qiguo. Clustering Web Users Based on Users Browsing Action [J]. Computer Science, 2008, 35(3): 186-187.)
[18] 孙吉贵, 刘杰, 赵连宇. 聚类算法研究[J]. 软件学报, 2008, 19(1): 48-58. (Sun Jigui, Liu Jie, Zhao Lianyu. Clustering Algorithms Research [J]. Journal of Software, 2008, 19(1): 48-58.)
[19] 天池数据实验室[EB/OL]. [2015-05-25]. http://tianchi.aliyun. com/datalab/dataSet.htm?spm=5176.100073.888.7.u6vPAh&id=2. (Tianchi DataLab [EB/OL]. [2015-05-25]. http://tianchi.
[20] ICTCLAS [EB/OL]. [2014-11-28].

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