Abstract
A wealth of previous literature has shown the inequalities gap between task requesters’ and workers’ decision support provided in crowdsourcing platforms. On the one hand, many platforms allow requesters to assess worker performance data and support the gauging of qualification criterion in order to control the quality of crowd submissions. On the other hand, workers are usually provided with very limited support throughout the task selection and completion processes. In particular, most workers must manually browse through a long list of open tasks before they determine which ones to sign up. Manual task selection not only is time consuming, but also tends to be sub optimal due to subjective, ad hoc worker behaviors.
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Wang, Q., Chen, Z., Wang, J., Feng, Y. (2022). Task Recommendation for Crowd Worker. In: Intelligent Crowdsourced Testing. Springer, Singapore. https://doi.org/10.1007/978-981-16-9643-5_5
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DOI: https://doi.org/10.1007/978-981-16-9643-5_5
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