Teams Generation System for Collaborative Works in Crowdsourcing

Abstract

A crowdsourcing with cooperative collaboration among experts should be performed by teams considered with the compatibility of crowd workers. In this study, we develop a system to automatically form an organization which can efficiently carry out complex and large-scale projects in crowdsourcing. The system represents the compatibilities among workers based on their past collaborative works as a social network. We also develop an algorithm based on the greedy method to search teams with the compatibility optimization under the constraints of budget and skills. In the experiment, we collect 169627 users on 33983 repositories from GitHub, and determine the social network and skills of the workers by the used programming languages and contributions for the repositories. The characteristics of the developed system are observed by the simulation experiments with a virtual project with skills and budget requirements. In particular, as a result of comparison on teams formed with the minimum budget, the proposed team formation algorithm achieves 96% higher in the compatibility of teams compared to the algorithm without considering compatibility.