Abstract
Crowdsourcing engages a workforce to accomplish complex tasks regardless of geographical limitation and is now growing rapidly in a variety of areas. On the one hand the selection of a wide array of workers has created a competitive and flexible market that suits well the needs of different types of task publishers, on the other hand, it is hard to select workers that satisfy the requirements of the task publishers best among a large number of workers. As such, task-worker matching plays a crucial role in crowdsourcing lifecycle. In this paper, we present a solution that enables customizing task description and adaptive task matching for software crowd work. An extensible meta-model is proposed to support description of both worker skills and task requirements. Based on this meta-model, we define an algorithm that allows self-adaptive matching of the task requirements against the worker skills. Further, several workers will be chosen to form a team once a single individual doesn’t meet the requirements of the task. A full experimental validation with four tasks and thousands of workers has been done showing the validation of our solution.
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Acknowledgements
This paper is supported by the program of University-Industry Cooperation of Shanghai under Granted No. Hu-CXY-2014-013 and the National Natural Science Foundation of China under Granted No. 61472242.
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Fu, Y., Chen, H., Song, F. (2015). STWM: A Solution to Self-adaptive Task-Worker Matching in Software Crowdsourcing. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9528. Springer, Cham. https://doi.org/10.1007/978-3-319-27119-4_27
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DOI: https://doi.org/10.1007/978-3-319-27119-4_27
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