IEICE TRANSACTIONS on Information
Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors
Summary :
This paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.10 pp.2659-2663
- Publication Date
- 2017/10/01
- Publicized
- 2017/07/10
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8094
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Sang-Chul LEE
Carnegie Mellon University
Christos FALOUTSOS
Carnegie Mellon University
Dong-Kyu CHAE
Hanyang University
Sang-Wook KIM
Hanyang University
Keyword
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Sang-Chul LEE, Christos FALOUTSOS, Dong-Kyu CHAE, Sang-Wook KIM, "Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 10, pp. 2659-2663, October 2017, doi: 10.1587/transinf.2017EDL8094.
Abstract: This paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8094/_p
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@ARTICLE{e100-d_10_2659,
author={Sang-Chul LEE, Christos FALOUTSOS, Dong-Kyu CHAE, Sang-Wook KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors},
year={2017},
volume={E100-D},
number={10},
pages={2659-2663},
abstract={This paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset.},
keywords={},
doi={10.1587/transinf.2017EDL8094},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors
T2 - IEICE TRANSACTIONS on Information
SP - 2659
EP - 2663
AU - Sang-Chul LEE
AU - Christos FALOUTSOS
AU - Dong-Kyu CHAE
AU - Sang-Wook KIM
PY - 2017
DO - 10.1587/transinf.2017EDL8094
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2017
AB - This paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset.
ER -
English
