Task composition in crowdsourcing

Sihem Amer-Yahia, Eric Gaussier, Vincent Leroy, Julien Pilourdault, Ria Mae Borromeo, Motomichi Toyama

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

6 Citations (Scopus)

Abstract

Crowdsourcing has gained popularity in a variety of domains as an increasing number of jobs are 'taskified' and completed independently by a set of workers. A central process in crowdsourcing is the mechanism through which workers find tasks. On popular platforms such as Amazon Mechanical Turk, tasks can be sorted by dimensions such as creation date or reward amount. Research efforts on task assignment have focused on adopting a requester-centric approach whereby tasks are proposed to workers in order to maximize overall task throughput, result quality and cost. In this paper, we advocate the need to complement that with a worker-centric approach to task assignment, and examine the problem of producing, for each worker, a personalized summary of tasks that preserves overall task throughput. We formalize task composition for workers as an optimization problem that finds a representative set of k valid and relevant Composite Tasks (CTs). Validity enforces that a composite task complies with the task arrival rate and satisfies the worker's expected wage. Relevance imposes that tasks match the worker's qualifications. We show empirically that workers' experience is greatly improved due to task homogeneity in each CT and to the adequation of CTs with workers' skills. As a result task throughput is improved.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages194-203
Number of pages10
ISBN (Electronic)9781509052066
DOIs
Publication statusPublished - 2016 Dec 22
Event3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 - Montreal, Canada
Duration: 2016 Oct 172016 Oct 19

Publication series

NameProceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016

Other

Other3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
CountryCanada
CityMontreal
Period16/10/1716/10/19

Keywords

  • Clustering
  • Crowdsourcing
  • Fuzzy
  • Task-composition

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence

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  • Cite this

    Amer-Yahia, S., Gaussier, E., Leroy, V., Pilourdault, J., Borromeo, R. M., & Toyama, M. (2016). Task composition in crowdsourcing. In Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 (pp. 194-203). [7796905] (Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2016.27