Task composition in crowdsourcing

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

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ194-203
ページ数10
ISBN(電子版)9781509052066
DOI
出版ステータスPublished - 2016 12 22
イベント3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 - Montreal, Canada
継続期間: 2016 10 172016 10 19

出版物シリーズ

名前Proceedings - 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

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence

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