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Volume 32 Issue 1
January  2017
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Yu Zhou, Jian-Bin Huang, Xiao-Lin Jia, He-Li Sun. On Participation Constrained Team Formation[J]. Journal of Computer Science and Technology, 2017, 32(1): 139-154. DOI: 10.1007/s11390-017-1710-6
Citation: Yu Zhou, Jian-Bin Huang, Xiao-Lin Jia, He-Li Sun. On Participation Constrained Team Formation[J]. Journal of Computer Science and Technology, 2017, 32(1): 139-154. DOI: 10.1007/s11390-017-1710-6
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On Participation Constrained Team Formation

  • 1 School of Computer Science and Technology, Xidian University, Xi'an 710071, China;
    2 School of Software, Xidian University, Xi'an 710071, China;
    3 Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China

Funds: The work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472299, 61540008, 61672417 and 61602354, the Fundamental Research Funds for the Central Universities of China under Grant No. BDY10, the Shaanxi Postdoctoral Science Foundation, and the Natural Science Basic Research Plan of Shaanxi Province of China under Grant No. 2014JQ8359.
More Information
  • Author Bio:

    Yu Zhou is a Ph.D. student in the School of Computer Science and Technology at Xidian University, Xi'an. His research interests include data mining, statistical machine learning and heterogeneous information network.

  • Corresponding author:

    Jian-Bin Huang E-mail: jbhuang@xidian.edu.cn

  • Received Date: January 26, 2016
  • Revised Date: September 20, 2016
  • Published Date: January 04, 2017
    Abstract
  • The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment problem related to the online labor market, termed as ClusterHire. We improve the definition of the ClusterHire problem, and propose an efficient and effective algorithm, entitled Influence. In addition, we place a participation constraint on ClusterHire. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained ClusterHire problem, we devise two algorithms, named ProjectFirst and Era. The former generates a participationconstrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1) Influence performs better than the state-of-the-art algorithms in terms of effectiveness and time efficiency; 2) ProjectFirst performs better than Era in terms of time efficiency, yet Era performs better than ProjectFirst in terms of effectiveness.
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