Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.elerap.2013.01.001 | Electronic Commerce Research and Applications |
Keywords | Field | DocType |
Pricing fraud,Fraud detection,Online shopping,e-Commerce,Expectation maximization algorithm,Finite mixture model | Anomaly detection,Service quality,Computer science,Expectation–maximization algorithm,Commerce,Unsupervised learning,Hidden variable theory,Mixture model,Marketing,E-commerce,Special case | Journal |
Volume | Issue | ISSN |
12 | 3 | 1567-4223 |
Citations | PageRank | References |
3 | 0.56 | 52 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kwanho Kim | 1 | 361 | 37.49 |
| Ye Rim Choi | 2 | 9 | 1.49 |
| Jonghun Park | 3 | 491 | 37.86 |