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.
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Acknowledgements
This work was supported by the National Key R&D Program of China (2019YFB1405302), the State Key Laboratory of Computer Software New Technology Open Project Fund (KFKT2018B05), the NSFC (Grant No. 61872072). Baiyou Qiao is supported by the National Key R&D Program of China (2016YFC1401900).
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Gang Wu is an associate professor of the School of Computer Science and Engineering at Northeastern University, China. He received his BS and MS degrees from Northeastern University, China in 2000 and 2003, respectively, and his PhD degree from Tsinghua University, China in 2008. His main research interests include main memory database, knowledge graph, and social networks. He is a member of ACM, a member of Chinese Information Processing Society of China, and a member of China Computer Federation.
Zhiyong Chen received his MS degree from the School of Computer Science and Engineering at Northeastern University, China in 2018. During the period in Northeastern University, his main research direction was the social networks based recommendation system, especially the problem of task assignment in crowdsourcing.
Jia Liu received his MS degree from the School of Computer Science and Engineering at Northeastern University, China in 2017. He worked in Shanghai Research & Development Center of Baidu Inc. from 2017 to 2019. His main research direction was the social networks based recommendation system and crowdsourcing.
Donghong Han received the PhD degree in computer science from Northeastern University, China in 2007. She is currently an associate professor of the School of Computer Science and Engineering at Northeastern University, China. Her research interests include data stream management, data mining and social networks sentiment analysis.
Baiyou Qiao is an associate professor of the School of Computer Science and Engineering at Northeastern University, China. He is a member of China Computer Federation. His research interests include cloud computing, virtualization technology, big data and spatial data management.
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Wu, G., Chen, Z., Liu, J. et al. Task assignment for social-oriented crowdsourcing. Front. Comput. Sci. 15, 152316 (2021). https://doi.org/10.1007/s11704-019-9119-8
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DOI: https://doi.org/10.1007/s11704-019-9119-8