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http://dx.doi.org/10.14400/JDC.2017.15.2.115

Credibility Enhancement of Online Reputation Systems for SNS Using Collaborative Filtering Method  

Cho, Jin-hyung (Dept. of Computer & Information Engineering, Dongyang Mirae University)
Kang, Hwan-Soo (Dept. of Computer & Information Engineering, Dongyang Mirae University)
Kim, Sea-Woo (Dept. of Family Welfare, Soongeui Women's College)
Publication Information
Journal of Digital Convergence / v.15, no.2, 2017 , pp. 115-120 More about this Journal
Abstract
Online reputation systems for social network services(SNS) aggregate users' feedback and estimate the reputation of contents or providers. The aim of this research is to enhance credibility of the online reputation system on the SNS based e-Commerce(we called it as social commerce). SNS users usually refer to evaluations from other users who bought the products before. Most social commerce sites provide reputation system to help their customer make a decision, but sometimes we can't believe the reputation because the reputation is too subjective and the seller can deceive the customer for sales promotion. Threrefore, we usually use just the average value to show the general customer's evaluation result. We applied collaborative filtering method to give more weighting to the users who have evaluated correctly in the past. As a result, we could get more accurate evaluation results by considering each customers' credibility value that was computed by collaborative filtering.
Keywords
Reputation system; Collaborative filtering; Social network services(SNS); Credibility; Social commerce;
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Times Cited By KSCI : 2  (Citation Analysis)
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