Research on Task Pricing of Crowdsourcing Platform
Senrong Ma, Lei Wang, Jiaen Guo, Gang Zhou
Available Online February 2018.
- https://doi.org/10.2991/csece-18.2018.113How to use a DOI?
- partial binary tree support vector machine; genetic algorithm; crowdsourcing platform
- With the development of the science and technology, a new business model for publishing tasks over the internet has emerged. This paper mainly focuses on the task completion of crowdsourcing platform and pricing scheme. By packaging tasks centrally, we establish a task pricing model based on partial least square regression and partial binary tree support vector machine. Traditional pricing schemes only consider the benefits of the platform but neglect the benefits of the users. Thus, it has the problem of low completion rate and dispersion of tasks. Comparing with the traditional pricing scheme, this model can not only improve the task completion rate but also maximize the benefits of both the task platform and the user. Besides, the model can avoid the problem of user congestion and make the model more secure and reliable.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Senrong Ma AU - Lei Wang AU - Jiaen Guo AU - Gang Zhou PY - 2018/02 DA - 2018/02 TI - Research on Task Pricing of Crowdsourcing Platform PB - Atlantis Press SP - 518 EP - 521 SN - 2352-538X UR - https://doi.org/10.2991/csece-18.2018.113 DO - https://doi.org/10.2991/csece-18.2018.113 ID - Ma2018/02 ER -