Robust and High-Accessibility Ranking Method for Crowdsourcing-Based Decision Making

With the advancement of online technologies in recent years, crowdsourcing data has been used for numerous applications in many fields. The preference sequences obtained through crowdsourcing are valuable resources for ranking. However, the aggregation of incomplete and inconsistent preferences is c...

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Bibliographic Details
Published in:Group decision and negotiation 2023-10, Vol.32 (5), p.1211-1236
Main Authors: Nguyen, Phan-Anh-Huy, Hsu, Ping-Yu
Format: Article
Language:English
Subjects:
Access
Algorithms
Biological and Physical Anthropology
Business and Management
Collaboration
Crowdfunding
Crowdsourcing
Decision making
Design
Internet
Methods
Preferences
Ratings & rankings
Robustness
Sequences
Simulation
Sparsity
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Summary:With the advancement of online technologies in recent years, crowdsourcing data has been used for numerous applications in many fields. The preference sequences obtained through crowdsourcing are valuable resources for ranking. However, the aggregation of incomplete and inconsistent preferences is complicated. To address these challenges, this study proposed a novel method termed robust crowd ranking (RCR) based on a consistent fuzzy c-means approach to increase the robustness and accessibility of aggregated preference sequences obtained through crowdsourcing. To verify the robustness, accessibility, and accuracy of RCR, comprehensive experiments were conducted using synthetic and real data. The simulation results validated that the RCR outperforms Borda Count, Dodgson, IRV and Tideman methods.
ISSN:0926-2644
1572-9907
DOI:10.1007/s10726-023-09840-2