Please use this identifier to cite or link to this item:
Title: Filtering unfair ratings from dishonest advisors in multi-criteria e-markets: a biclustering-based approach
Authors: Zhang, Jie
Irissappane, Athirai Aravazhi
Keywords: Multiple criteria
Electronic marketplaces
Unfair rating attack
Issue Date: 2015
Source: Irissappane, A. A., & Zhang, J. Filtering unfair ratings from dishonest advisors in multi-criteria e-markets: a biclustering-based approach. Autonomous Agents and Multi-Agent Systems, in press.
Series/Report no.: Autonomous Agents and Multi-Agent Systems
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.
ISSN: 1387-2532
DOI: 10.1007/s10458-015-9314-4
Schools: School of Computer Engineering 
Rights: © 2015 The Author(s).
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Citations 50

Updated on Jun 16, 2024

Web of ScienceTM
Citations 50

Updated on Oct 30, 2023

Page view(s) 20

Updated on Jun 15, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.