iCLUB: An Integrated Clustering-Based Approach to Improve the Robustness of Reputation Systems(Extended Abstract)

摘要

The problem of unfair testimonies has to be addressed effectively to improve the robustness of reputation systems. We propose an integrated CLUstering-Based approach called iCLUB to filter unfair testimonies for reputation systems using multi-nominal testimonies, in multiagent-based electronic commerce. It adopts clustering and considers buying agents' local and global knowledge about selling agents. Experimental evaluation demonstrates promising results of our approach in filtering various types of unfair testimonies.