Multi-Team Formation System for Collaborative Crowdsourcing

For complex crowdsourcing tasks that require collaboration among multiple individuals, teams should be formed by considering both worker compatibility and expertise. Furthermore, the nature of crowdsourcing dictates the budget for tasks and worker remuneration, and excessively large teams may reduce collaborative performance. To address these challenges, we propose a heuristic optimization algorithm that leverages social network information to simultaneously form teams with optimized worker compatibility for multiple tasks. In our approach, historical collaboration is represented as a social network in which the edge weights correspond to the explicit ratings of worker compatibility. In a simulation experiment using synthetic data, we applied Gaussian process regression to examine the relationship between the eight experimental parameters and evaluation values, thereby analyzing the output of the proposed algorithm. To generate the data necessary for regression, we ran the proposed algorithm with experimental parameters that were sequentially estimated using Bayesian optimization. Our experiments revealed that the evaluation values were extremely low when the team size limit, degree mean of the social network, and task budget were set to low values. The results also indicate that the proposed algorithm outperforms the hill-climbing method under almost all experimental conditions. In addition, the highest evaluation values were achieved when the simulated annealing temperature decreased at a rate of approximately 0.9, whereas smoothing the objective function proved to be ineffective.