Feedback Based High-Quality Task Assignment in Collaborative Crowdsourcing

Liang Qiao, Feilong Tang, Jiacheng Liu

Abstract: Collaborative Crowdsourcing focuses on tasks that need to be finished by a group of workers, usually is required to get information about worker affinities. However, there are few works study the problem how to get the accurate estimation of worker affinity along with worker skill. Under this situation, the task assignment problem in a cold-start collaborative crowdsourcing platform will be even harder. In this paper, we design a novel collaborative crowdsourcing framework that allows task requesters as well as workers to provide feedback to the task executors or co-workers. Using the information from feedbacks, we build the model to estimate worker affinity and worker skill more accurately and based on which we propose a measurement to measure the matching degree between task and a group of workers. Next, we propose a heuristic task assignment called FCC-SA which averagely distribute skillful workers to all collaborative tasks. Finally, we hire crowd workers to conduct real experiments to test our model and algorithm. Experimental results demonstrate that our FCC-SA algorithm significantly outperforms representative related proposals, and our novel measurement also has better precision than traditional measure method.