Task Composition in Crowdsourcing

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.
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Communication dans un congrès
International Conference on Data Science and Advanced Analytics, Oct 2016, Montreal, Canada. Proceedings of the International Conference on Data Science and Advanced Analytics, 2016, Proceedings of the International Conference on Data Science and Advanced Analytics. <https://sites.ualberta.ca/~dsaa16/>
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Soumis le : vendredi 2 décembre 2016 - 15:15:42
Dernière modification le : jeudi 8 décembre 2016 - 01:02:59
Document(s) archivé(s) le : lundi 20 mars 2017 - 19:36:05

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Sihem Amer-Yahia, Eric Gaussier, Vincent Leroy, Julien Pilourdault, Ria Borromeo, et al.. Task Composition in Crowdsourcing. International Conference on Data Science and Advanced Analytics, Oct 2016, Montreal, Canada. Proceedings of the International Conference on Data Science and Advanced Analytics, 2016, Proceedings of the International Conference on Data Science and Advanced Analytics. <https://sites.ualberta.ca/~dsaa16/>. <hal-01407780>

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