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AN INTEGRATED CLUSTERING‐BASED APPROACH TO FILTERING UNFAIR MULTI‐NOMINAL TESTIMONIES
Author(s) -
Liu Siyuan,
Zhang Jie,
Miao Chunyan,
Theng YinLeng,
Kot Alex C.
Publication year - 2014
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00464.x
Subject(s) - reputation , robustness (evolution) , computer science , collusion , cluster analysis , reputation system , artificial intelligence , binary number , filter (signal processing) , data mining , mathematics , sociology , microeconomics , economics , computer vision , biochemistry , chemistry , arithmetic , gene , social science
Reputation systems have contributed much to the success of electronic marketplaces. However, the problem of unfair testimonies has to be addressed effectively to improve the robustness of reputation systems. Until now, most of the existing approaches focus only on reputation systems using binary testimonies, and thus have limited applicability and effectiveness. In this paper, We propose an i ntegrated CLU stering‐ B ased approach called iCLUB to filter unfair testimonies for reputation systems using multinominal testimonies, in an example application of multiagent‐based e‐commerce. It adopts clustering techniques and considers buyer agents’ local as well as global knowledge about seller agents. Experimental evaluation demonstrates the promising results of our approach in filtering various types of unfair testimonies, its robustness against collusion attacks, and better performance compared to competing models.