Start Date
12-13-2015
Description
Reputation systems could help consumers avoid transaction risk by providing historical consumers’ feedback. But, traditional reputation systems are vulnerable to the rating manipulation. It will undermine the trustworthiness of the reputation systems and users’ satisfaction will be lost. To address the issue, this study uses the real-world rating data from two travel website: Tripadvisor.com and Expedia.com and one e-commerce website Amazon.com to empirically exploit the features of fraudulent raters. Based on those features, it proposes the new method for fraudulent rater detection. First, it examines the received rating series of each entity and filter out the entity which is under attack (termed as target entity). Second, the clustering based method is applied to discriminate fraudulent raters. Experimental studies have shown that the proposed method is effective in detecting the fraudulent raters accurately while keeping the majority of the normal users in the systems in various attack environment settings.
Recommended Citation
Cai, Yuanfeng and Zhu, Dan, "Rating Fraud Detection---Towards Designing a Trustworthy Reputation Systems" (2015). ICIS 2015 Proceedings. 19.
https://aisel.aisnet.org/icis2015/proceedings/DecisionAnalytics/19
Rating Fraud Detection---Towards Designing a Trustworthy Reputation Systems
Reputation systems could help consumers avoid transaction risk by providing historical consumers’ feedback. But, traditional reputation systems are vulnerable to the rating manipulation. It will undermine the trustworthiness of the reputation systems and users’ satisfaction will be lost. To address the issue, this study uses the real-world rating data from two travel website: Tripadvisor.com and Expedia.com and one e-commerce website Amazon.com to empirically exploit the features of fraudulent raters. Based on those features, it proposes the new method for fraudulent rater detection. First, it examines the received rating series of each entity and filter out the entity which is under attack (termed as target entity). Second, the clustering based method is applied to discriminate fraudulent raters. Experimental studies have shown that the proposed method is effective in detecting the fraudulent raters accurately while keeping the majority of the normal users in the systems in various attack environment settings.