A quality enhancement of crowdsourcing based on quality evaluation and user-level task assignment framework

Crowdsourcing has recently been used in various applications, and the possibility of its utilization and importance is expected to increase continuously in the future. However, crowdsourcing cannot always ensure the precision of the results, which are generated by unspecified individuals. In particular, a more sophisticated task has more complex problems that are related to the accuracy of the result. In this paper, we propose a novel framework to improve the quality of work in a crowdsourcing environment. In this framework, we analyzes the characteristics of workers and allocates the appropriate task to individuals to improve the quality of work. It also provides cumulative voting for correct assessment instead of the majority representation method, which is more commonly used. Our experiments show that this framework facilitates effective work allocation.