TY - JOUR
T1 - Pricing fraud detection in online shopping malls using a finite mixture model
AU - Kim, Kwanho
AU - Choi, Yerim
AU - Park, Jonghun
PY - 2013/5
Y1 - 2013/5
N2 - 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.
AB - 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.
KW - e-Commerce
KW - Expectation maximization algorithm
KW - Finite mixture model
KW - Fraud detection
KW - Online shopping
KW - Pricing fraud
UR - http://www.scopus.com/inward/record.url?scp=84878610508&partnerID=8YFLogxK
U2 - 10.1016/j.elerap.2013.01.001
DO - 10.1016/j.elerap.2013.01.001
M3 - Article
AN - SCOPUS:84878610508
SN - 1567-4223
VL - 12
SP - 195
EP - 207
JO - Electronic Commerce Research and Applications
JF - Electronic Commerce Research and Applications
IS - 3
ER -