Research on Task Pricing of Crowdsourcing Platform

DOI

10.2991/csece-18.2018.113How to use a DOI?

Keywords

partial binary tree support vector machine; genetic algorithm; crowdsourcing platform

Abstract

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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
BT  - Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SP  - 518
EP  - 521
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.113
DO  - 10.2991/csece-18.2018.113
ID  - Ma2018/02
ER  -