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2022 | OriginalPaper | Chapter

5. Task Recommendation for Crowd Worker

Authors : Qing Wang, Zhenyu Chen, Junjie Wang, Yang Feng

Published in: Intelligent Crowdsourced Testing

Publisher: Springer Nature Singapore

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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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previous chapter Characterization of Crowd Worker
next chapter Crowd Worker Recommendation for Testing Task
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Metadata
Title
Task Recommendation for Crowd Worker
Authors
Qing Wang
Zhenyu Chen
Junjie Wang
Yang Feng
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Intelligent Crowdsourced Testing

Print ISBN: 978-981-16-9642-8

Electronic ISBN: 978-981-16-9643-5

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-16-9643-5_5

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