Pricing fraud detection in online shopping malls using a finite mixture model

作     者：Kim, Kwanho Choi, Yerim Park, Jonghun 

作者机构：Seoul Natl Univ Dept Ind Engn Seoul 151744 South Korea 

出 版 物：《ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS》 (电子商务研究及应用)

年 卷 期：2013年第12卷第3期

页      面：195-207页

核心收录：

学科分类：1202[管理学-工商管理] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Research Foundation of Korea (NRF) Korea Government (MEST) [2012-0002032, 2011-0030814] Engineering Research Institute at Seoul National University Interpark INT 

主　　题：Pricing fraud Fraud detection Online shopping e-Commerce Expectation maximization algorithm Finite mixture model 

摘      要：Although pricing fraud is an important issue for improving service quality of online shopping malls, research on automatic fraud detection has been limited. In this paper, we propose an unsupervised learning method based on a finite mixture model to identify pricing frauds. We consider two states, normal and fraud, for each item according to whether an item description is relevant to its price by utilizing the known number of item clusters. Two states of an observed item are modeled as hidden variables, and the proposed models estimate the state by using an expectation maximization (EM) algorithm. Subsequently, we suggest a special case of the proposed model, which is applicable when the number of item clusters is unknown. The experiment results show that the proposed models are more effective in identifying pricing frauds than the existing outlier detection methods. Furthermore, it is presented that utilizing the number of clusters is helpful in facilitating the improvement of pricing fraud detection performances. (C) 2013 Elsevier B.V. All rights reserved.