Reducing uncertainty of probabilistic Top-K ranking via Pairwise crowdsourcing

Xin Lin, Jianliang Xu, Haibo Hu, Fan Zhe

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a novel pairwise crowd-sourcing model to reduce the uncertainty of top-k ranking using a crowd of domain experts. Given a crowdsourcing task of limited budget, we propose efficient algorithms to select the best object pairs for crowdsourcing that will bring in the highest quality improvement. Extensive experiments show that our proposed solutions outperform a random selection method by up to 30 times in terms of quality improvement of probabilistic top-k ranking queries. In terms of efficiency, our proposed solutions can reduce the elapsed time of a brute-force algorithm from several days to one minute.

Original languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1757-1758
Number of pages2
ISBN (Electronic)9781538655207
DOIs
Publication statusPublished - 16 Apr 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018

Publication series

NameProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

Conference

Conference34th IEEE International Conference on Data Engineering, ICDE 2018
Country/TerritoryFrance
CityParis
Period16/04/1819/04/18

Keywords

  • Crowdsourcing
  • Top k
  • Uncertain query

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems

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