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Maximizing user type diversity for task assignment in crowdsourcing

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Abstract

Crowdsourcing employs numerous users to perform certain tasks, in which task assignment is a challenging issue. Existing researches on task assignment mainly consider spatial–temporal diversity and capacity diversity, but not focus on the type diversity of users, which may lead to low quality of tasks. This paper formalizes a novel task assignment problem in crowdsourcing, where a task needs the cooperation of various types of users, and the quality of a task is highly related to the various types of the recruited users. Therefore, the goal of the problem is to maximize the user type diversity subject to limited task budget. This paper uses three heuristic algorithms to try to resolve this problem, so as to maximize user type diversity. Through extensive evaluation, the proposed algorithm Unit Reward-based Greedy Algorithm by Type obviously improves the user type diversity under different user type distributions.

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Acknowledgements

This work is partly supported by the National Natural Science Foundation of China under Grant Nos. 61977044, 61872228 and 61877037, the National Key R&D Program of China under Grant No. 2017YFB1402102, the Key R&D Program of Shaanxi Province under Grant No. 2020GY-221, 2019ZDLSF07-01, 2020ZDLGY10-05, the Natural Science Basis Research Plan in Shaanxi Province of China under Grant Nos. 2020JM-303, 2020JM-302, 2017JM6060), the Fundamental Research Funds for the Central Universities of China under Grant No. GK201903090, GK201801004, the S&T Plan of Xi’an City of China under Grant No. 2019216914GXRC005CG006-GXYD5.1, the Shaanxi Normal University Foundational Education Course Research Center of Ministry of Education of China under Grant No. 2019-JCJY009.

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  1. Ana Wang and Meirui Ren have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Ana Wang, Lichen Zhang, Peng Li & Longjiang Guo

  2. Engineering Laboratory of Teaching Information Technology of Shaanxi Province, Xi’an, 710119, China

    Ana Wang, Lichen Zhang, Peng Li & Longjiang Guo

  3. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Ana Wang, Meirui Ren, Hailong Ma, Lichen Zhang, Peng Li & Longjiang Guo

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  1. Ana Wang
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  2. Meirui Ren
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  3. Hailong Ma
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  4. Lichen Zhang
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  5. Peng Li
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  6. Longjiang Guo
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Correspondence to Lichen Zhang or Longjiang Guo.

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Wang, A., Ren, M., Ma, H. et al. Maximizing user type diversity for task assignment in crowdsourcing. J Comb Optim 40, 1092–1120 (2020). https://doi.org/10.1007/s10878-020-00645-6

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