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 prob...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.展开更多
基金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.
文摘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.
