NDLI: Building Robust Reputation Systems in the E-commerce Environment

Content Provider	IEEE Xplore Digital Library
Author	Huiying Duan Feifei Liu
Copyright Year	2012
Abstract	In the environment of e-commerce, building a robust Reputation System is a task of not only constructing an effective reputation model in terms of manipulation resistance, but also designing efficient mechanisms to detect customers and vendors whose behavior arouses suspicion. Given context of reputation, we can extract characters or assumptions from the target system. Based on these characters, an advanced reputation model, R-Rep, is proposed to resist manipulative behavior. Clustering algorithm k-means is used to identify suspicious vendors and customers. Results indicate that, R-Rep outperforms two existing models, the reputation model employed by Taobao (the largest e-commerce site in China) and a Bayesian System. By using R-Rep, negative influence imposed by suspects on Reputation Systems is largely restricted over time. Meanwhile, via comparing between statistics of the whole population and suspicious subpopulation, we explore the characteristics of suspicious customers and suspicious vendors intensively, and discover some interesting patterns and phenomena.
Starting Page	326
Ending Page	333
File Size	360437
Page Count	8
File Format	PDF
ISBN	9781467321723
DOI	10.1109/TrustCom.2012.101
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-25
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Reputation System Bayesian methods Buildings Clustering algorithms Manipulative behavior detection Trust Management System Robustness Marketing and sales Complexity theory Taobao
Content Type	Text
Resource Type	Article

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