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An Empirical Study of GraphQL Schemas Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Recommendation of Crowdsourcing Workers Based on Multi-community Collaboration

Authors : Zhifang Liao, Xin Xu, Peng Lan, Jun Long, Yan Zhang

Published in: Service-Oriented Computing

Publisher: Springer International Publishing

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Abstract

Currently there are problems such as fuzzy workers’ characteristics and complex human relations existing on many crowdsourcing platforms, which lead to the difficulty in the recommendation of workers to complete tasks on crowdsourcing platforms. Aiming at worker recommendations in categorical tasks on crowdsourcing platforms, this paper proposes a recommendation considering workers’ multi-community characteristics. It takes factors such as worker’s reputation, preference and activity into consideration. Finally, based on the characteristics of community intersections, it recommends Top-N workers. The results show the recommendations generated by the algorithm proposed in this paper performs the best comprehensively.

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Metadata
Title
A Recommendation of Crowdsourcing Workers Based on Multi-community Collaboration
Authors
Zhifang Liao
Xin Xu
Peng Lan
Jun Long
Yan Zhang
Copyright Year
2019
Book
Service-Oriented Computing

Print ISBN: 978-3-030-33701-8

Electronic ISBN: 978-3-030-33702-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-33702-5_34

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