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Filtering unfair ratings from dishonest advisors in multi-criteria e-markets: a biclustering-based approach

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Abstract

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.

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Notes

  1. While it is possible to re-interpret the opinions of the subjectively different advisors to suit the preferences of the active buyer \(\text {b}\) [5], we will consider this as a potential area for future work.

  2. Rank vector is a vector of the rank of ratings for all criteria.

  3. All claims made in the text are statistically significant according to the Paired-Samples T test with \(\alpha =0.05\) (the T test is a statistical method that allows us to confirm within a predefined confidence level whether the means of two groups are significantly different from each other [3]). Specifically, we use the performance (MAE/MCC) values, while determining the trustworthiness of each seller, between pairs of approaches to assess the statistical significance of the results.

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Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Athirai Aravazhi Irissappane & Jie Zhang

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  1. Athirai Aravazhi Irissappane
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  2. Jie Zhang
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Correspondence to Athirai Aravazhi Irissappane.

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Aravazhi Irissappane, A., Zhang, J. Filtering unfair ratings from dishonest advisors in multi-criteria e-markets: a biclustering-based approach. Auton Agent Multi-Agent Syst 31, 36–65 (2017). https://doi.org/10.1007/s10458-015-9314-4

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