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

Phan Anh Huy Nguyen, Ping Yu Hsu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1211-1236
Number of pages26
JournalGroup Decision and Negotiation
Issue number5
StatePublished - Oct 2023


  • Accessibility
  • Crowd ranking
  • Crowdsourcing
  • Fuzzy c-means
  • Robust ranking


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