Concurrent Team Formation for Multiple Tasks in Crowdsourcing Platform

The tremendous growth of social media technologies has inspired research communities as well as industries to extend the horizon of organizations by recruiting workers available on freelancing sites. Most of the tasks usually require expertise of workers from diverse domains, thus the problem can be reduced to that of team formation. In this work, we address the problem of assigning workers to tasks, where each task requires a set of skills and thus may require more than one worker to successfully complete the task. Given a set of tasks, and a set of workers each with a cost, the objective is to find mutually exclusive set of workers for each task, who can accomplish the task in the most cost-effective manner. The problem being NP-hard, we propose an approximation algorithm that attempts to find the best fit workers based on their collective intelligence for a single task. This approach selects workers in a manner that their expertise complements each other, hence maintaining a balance among the required skills. Such balanced assignment sets require lesser number of workers and reduce the overall cost. The approach is then extended to a set of N tasks. We show that the solution is (2+ α) approximate. The proposed algorithm is evaluated against different assignment schemes. Experimental results using real data show that our approach performs well.