An integrated clustering-based approach to filtering unfair multi-nominal testimonies

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 integrated CLUstering-Based 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.