Task assignment optimization in collaborative crowdsourcing

Habibur Rahman, Senjuti Basu Roy, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

A number of emerging applications, such as, collaborative document editing, sentence translation, and citizen journalism require workers with complementary skills and expertise to form groups and collaborate on complex tasks. While existing research has investigated task assignment for knowledge intensive crowdsourcing, they often ignore the aspect of collaboration among workers, that is central to the success of such tasks. Research in behavioral psychology has indicated that large groups hinder successful collaboration. Taking that into consideration, our work is one of the first to investigate and formalize the notion of collaboration among workers and present theoretical analyses to understand the hardness of optimizing task assignment. We propose efficient approximation algorithms with provable theoretical guarantees and demonstrate the superiority of our algorithms through a comprehensive set of experiments using real-world and synthetic datasets. Finally, we conduct a real world collaborative sentence translation application using Amazon Mechanical Turk that we hope provides a template for evaluating collaborative crowdsourcing tasks in micro-task based crowdsourcing platforms.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages949-954
Number of pages6
ISBN (Electronic)9781467395038
DOIs
StatePublished - Jan 5 2016
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
Country/TerritoryUnited States
CityAtlantic City
Period11/14/1511/17/15

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Algorithms
  • Collaborative crowdsourcing
  • Crowdsourcing
  • Optimization

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