A unified task recommendation strategy for realistic mobile crowdsourcing system

Abstract

A well-designed task recommendation framework aims to protect the data quality as well as increase the task execution results. However, current crowdsourcing systems ignore the fact that there are few duplicate task expectations because of the budget limitation in realistic conditions. Besides, a practical crowdsourcing system needs to recommend new tasks without previous knowledge about the concrete task content due to short task lifespan. Thus, most of the existing studies are not applicable due to the idealized assumptions. In this paper, we formally define the problem and prove it is NP-Hard. For the problem, we design a unified task recommendation system for realistic conditions to address the mentioned problems, Pioneer-Assisted Task RecommendatiON (PATRON) framework. The framework first selects a set of pioneer workers to collect initial knowledge of the new tasks. Then it adopts the k-medoids clustering algorithm to split the workers into subsets based on the worker similarity. Cluster selection and worker pruning provides accurate and efficient recommendations that satisfy the valid recommendation requirements from requesters. Finally, we conducted our experiments based on real datasets from a famous Chinese crowdsourcing platform, Tencent SOHO. The experimental results show the efficiency and accuracy of PATRON compared with three baseline methods from several perspectives, such as recommendation success rate and recommended worker quality.