Top-k Team Recommendation in Spatial Crowdsourcing

With the rapid development of Mobile Internet and Online To Offline (O2O) marketing model, various spatial crowdsourcing platforms, such as Gigwalk and Gmission, are getting popular. Most existing studies assume that spatial crowdsourced tasks are simple and trivial. However, many real crowdsourced tasks are complex and need to be collaboratively finished by a team of crowd workers with different skills. Therefore, an important issue of spatial crowdsourcing platforms is to recommend some suitable teams of crowd workers to satisfy the requirements of skills in a task. In this paper, to address the issue, we first propose a more practical problem, called \(\underline{Top}\)-\(\underline{k} \; \underline{T}eam \;\underline{R}ecommendation \;in \;spatial \;crowdsourcing\) (TopkTR) problem. We prove that the TopkTR problem is NP-hard and design a two-level-based framework, which includes an approximation algorithm with provable approximation ratio and an exact algorithm with pruning techniques to address it. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.