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Fraud Detection in Comparison-Shopping Services: Patterns and Anomalies in User Click Behaviors
Sang-Chul LEE Christos FALOUTSOS Dong-Kyu CHAE Sang-Wook KIM
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2017/10/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
fraud detection, comparison-shopping services, user behavior analysis,
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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.