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Worker Recommendation with High Acceptance Rates in Collaborative Crowdsourcing Systems

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2018)

Abstract

Crowdsourcing has emerged as a popular Internet-based collaboration paradigm, in which tasks published by requesters can be economically and efficiently accomplished by crowd workers. To ensure the quality of service (QoS) provided by crowd workers, requesters are more likely to assign tasks to trustworthy workers, therefore, trust have played an important role in the design of worker recommendation mechanisms in crowdsourcing systems. Most existing studies focus on the trust that requesters place on workers, however, which would suffer the low-acceptance problem because crowd workers would refuse to participate in tasks published by low-trustworthy requesters with a great probability. In order to address the low-acceptance problem, in this paper, by using biased matrix factorization, we proposed a novel worker recommendation mechanism which can evaluate mutual trust relationship between requesters and workers. And also, to accurately measure the matching degree between tasks and workers, a comprehensive and practical task matching mechanism has been presented by incorporating time matching, skill matching, payment matching, and location matching. Finally, extensive simulations and real data experiments highlight the performance of our proposed worker recommendation mechanism.

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Notes

  1. 1.

    https://www.wikipedia.org/.

  2. 2.

    https://www.mturk.com/.

  3. 3.

    http://www.gigwalk.com/.

  4. 4.

    http://www.zbj.com/.

  5. 5.

    https://pypi.org/project/scikit-surprise/1.0.3/.

  6. 6.

    https://snap.stanford.edu/data/soc-sign-epinions.html.

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Acknowledgements

The paper is supported by National Nature Science foundation of China under grant Nos.: 61572095 and 61877007.

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Authors and Affiliations

  1. School of Software Technology, Dalian University of Technology, Dalian, 116620, China

    Mingchu Li, Xiaomei Sun, Xing Jin & Linlin Tian

  2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, 116620, China

    Mingchu Li, Xiaomei Sun, Xing Jin & Linlin Tian

Authors
  1. Mingchu Li
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  2. Xiaomei Sun
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  3. Xing Jin
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  4. Linlin Tian
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Corresponding author

Correspondence to Xing Jin .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. University of West London, London, UK

    Xinheng Wang

  3. Hangzhou Dianzi University, Hangzhou Shi, Zhejiang, China

    Yuyu Yin

  4. London South Bank University, London, UK

    Muddesar Iqbal

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, M., Sun, X., Jin, X., Tian, L. (2019). Worker Recommendation with High Acceptance Rates in Collaborative Crowdsourcing Systems. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-12981-1_4

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-12981-1

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