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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2021, Vol. 15 Issue (2) : 152316    https://doi.org/10.1007/s11704-019-9119-8
RESEARCH ARTICLE
Task assignment for social-oriented crowdsourcing
Gang WU1,2(), Zhiyong CHEN1, Jia LIU1, Donghong HAN1, Baiyou QIAO1
1. School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China
2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
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Abstract

Crowdsourcing has become an efficient measure to solve machine-hard problems by embracing group wisdom, in which tasks are disseminated and assigned to a group of workers in the way of open competition. The social relationships formed during this process may in turn contribute to the completion of future tasks. In this sense, it is necessary to take social factors into consideration in the research of crowdsourcing. However, there is little work on the interactions between social relationships and crowdsourcing currently. In this paper, we propose to study such interactions in those social-oriented crowdsourcing systems from the perspective of task assignment. A prototype system is built to help users publish, assign, accept, and accomplish location-based crowdsourcing tasks as well as promoting the development and utilization of social relationships during the crowdsourcing. Especially, in order to exploit the potential relationships between crowdsourcing workers and tasks, we propose a “worker-task” accuracy estimation algorithm based on a graph model that joints the factorized matrixes of both the user social networks and the history “worker-task” matrix. With the worker-task accuracy estimation matrix, a group of optimal worker candidates is efficiently chosen for a task, and a greedy task assignment algorithm is proposed to further the matching of worker-task pairs among multiple crowdsourcing tasks so as to maximize the overall accuracy. Compared with the similarity based task assignment algorithm, experimental results show that the average recommendation success rate increased by 3.67%; the average task completion rate increased by 6.17%; the number of new friends added per week increased from 7.4 to 10.5; and the average task acceptance time decreased by 8.5 seconds.

Keywords crowdsourcing      social networks      task assignment     
Corresponding Author(s): Gang WU   
Just Accepted Date: 24 October 2019   Issue Date: 01 December 2020
 Cite this article:   
Gang WU,Zhiyong CHEN,Jia LIU, et al. Task assignment for social-oriented crowdsourcing[J]. Front. Comput. Sci., 2021, 15(2): 152316.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-9119-8
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I2/152316
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