Suitability-based Task Assignment in Crowdsourcing Markets

Abstract

Crowdsourcing web services has received much attention in recent years, which has been widely used in many fields, and provides important human computing services for the rapid development of AI. Crowdsourcing knowledge acquisition is one of the most important applications, which includes a series of work such as image annotation and picture classification. However, due to the differences in the difficulty of these tasks and the uncertainty of workers, how to make a reasonable task assignment while ensuring the completion of tasks becomes a big challenge. To this end, we introduce WordNet external knowledge base to help determine the difficulty of picture classification tasks. We refer to the e-sports rank mechanism and use dynamic update strategy to assess the actual ability of workers. A novel criterion affinity based on the weighted Euclidean distance with penalty factor is proposed to measure the suitability between tasks and workers. On this basis, the Kuhn-Munkres (KM) algorithm is used to solve the weighted bipartite graph matching problem. Through comparative experiments, the effectiveness of our proposed method is verified.