On Participation Constrained Team Formation

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 defin...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 32; no. 1; pp. 139 - 154
Main Authors Zhou, Yu, Huang, Jian-Bin, Jia, Xiao-Lin, Sun, He-Li
Format Journal Article
LanguageEnglish
Published New York Springer US 2017
Springer Nature B.V
School of Computer Science and Technology, Xidian University, Xi’an 710071, China%School of Software, Xidian University, Xi’an 710071, China%Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Subjects
Algorithms
Artificial Intelligence
Assignment problem
Computer Science
Constraints
Data Structures and Information Theory
Effectiveness
Efficiency
Hiring
Information Systems Applications (incl.Internet)
Internet
Labor
Labor market
Markets
Operations research
Participation
Regular Paper
Skills
Social networks
Social research
Software Engineering
Studies
Tasks
Teams
Theory of Computation
Universe
互联网
任务分配问题
劳动力市场
文件审查
时间效率
活动组织
算法
队形
team formation
task assignment
universal project-team map
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Summary: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 participation- constrained 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） INFLU- ENCE 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.
Bibliography: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 participation- constrained 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） INFLU- ENCE 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.
task assignment, team formation, universal project-team map
11-2296/TP
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-017-1710-6