Filtering unfair ratings from dishonest advisors in multi-criteria e-markets: a biclustering-based approach - Zhang, Jie (2016)
In multiagent e-markets, trust between interaction partners (buying agents and selling agents) is vital for any transaction to be successful. Given the difficulty for a buyer to directly judge the quality (trustworthiness) of a seller for a transaction, a buyer also seeks opinions from other buyers (called advisors) in the marketplace to determine the seller’s trustworthiness. However, advisors may act dishonestly by conveying misleading information about the seller. We propose a novel approach to identify such dishonest advisors, while evaluating a seller’s trustworthiness on multiple criteria. It is based on a biclustering method which clusters honest advisors on different criteria. Correlation between advisors’ ratings to various criteria is used as additional information to accurately filter dishonest advisors. A transitive mechanism is also employed in the biclustering process to cope with rating sparsity. Further, we introduce a parallelization technique to reduce the time complexity involved in the biclustering process. Detailed experiments in simulated environments demonstrate the robustness of the proposed approach against strategic attacks from dishonest advisors. Evaluation on three real datasets confirms the effectiveness of our approach in real environments.