2012/01/18 by Vibha Gaur, Gaur, Vibha, Neeraj Kumar Sharma +3
Computer Science · Social Sciences · #Access Control and Trust #Blockchain Technology Applications and Security #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Multiagent Systems (cs.MA) #Social and Information Networks (cs.SI) #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.1201.3835
openalex publication_date 2012/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Reputation systems aim to reduce the risk of loss due to untrustworthy participants. This loss is aggravated by dishonest advisors trying to pollute the e-market environment for their self-interest. A major task of a reputation system is to promote and encourage advisors who repeatedly respond with fair advice and to apply an opinion filtering or honesty checking mechanism to detect and resist dishonest advisors. This paper provides a dynamic approach to compute the aggregated shared reputation component by filtering out unfair advice and then generating the aggregated shared reputation value. The proposed approach is dynamic in nature as it is sensitive to the behaviour of advisors, value of the current transaction and encourages the cooperation among buyers as advisors. It provides incentive to honest advisors in lieu of repeated sharing of honest opinion by increasing the weight of their opinion and by making the increase in the reputation of honest advisors monotonically proportional to the value of a transaction.