Selecting workers like expert for crowdsourcing by integration evaluation of individual and collaborative abilities

Abstract

Team-based worker selection has been extensively studied for Mobile Crowdsourcing (MCS), in which a set of workers are recruited to form a team to complete complex tasks collaboratively. However, existing studies face two typical challenges: 1) how to dynamically evaluate workers’ individual abilities and collaborative contributions to the team; 2) how to select unknown workers to form a team with high quality at low cost. To tackle the above challenges, this paper proposes an Integration of Individual and Collaborative Abilities based Dynamic Worker Selection (IICA-DWS) algorithm to recruit excellent workers as a team in a high-quality and low-cost style. In the IICA-DWS algorithm, each worker’s individual ability and collaborative contribution to the team are evaluated more accurately using the Approximate Shapley Value (ASV). In addition, a high-quality team formation method is established to complete complex tasks at low cost. This involves the selection of both team leaders and team members. In this process, the Multi-Armed Bandit (MAB) model is adopted to dynamically select excellent workers using exploration and exploitation phases. Lastly, the IICA-DWS algorithm is evaluated through theoretical analysis and experimental results. The results show that the IICA-DWS algorithm can improve the data quality of tasks by 47.3% and reduce the cost by 61.7% on average. Moreover, the IICA-DWS algorithm has a high probability of approximating optimal results, which shows the best performance among the comparative algorithms.