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Although the aforementioned works study the task allocation problem on spatial crowdsourcing, they always assume that spatial crowdsourcing tasks are simple micro-tasks and ignore that some real spatial crowdsourced tasks often need to be collaboratively completed by a team of crowd workers.
Most works on spatial crowdsourcing study the task assignment problem.
As discussed above, we propose a novel team recommendation problem in spatial crowdsourcing, called the Top-k team recommendation in spatial crowdsourcing (TopkTR) problem.
In this paper, we study a novel spatial crowdsourcing problem, called the Top-k team recommendation in spatial crowdsourcing (TopkTR) and its variant, called the TopkTRL problem.
In this section, we review related works from two categories, spatial crowdsourcing and team formation.
Recently, the issue of online task assignment in dynamic spatial crowdsourcing scenarios is proposed [7].
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However, in real applications, there are many complex spatial crowdsourced tasks, which often need to be collaboratively completed by a team of crowd workers with different skills.
Note that the team-oriented spatial crowdsourced tasks studied in this paper, e.g., organizing a party, renovating a room, usually need to be completed in teams.
Digital humanities projects often use such methodologies and techniques as web-based media, digital archiving, data mining, geo-spatial analysis, crowdsourcing, data visualization, and simulation.
Digital humanities projects often use methodologies and techniques such as web-based media, digital archiving, data mining, geo-spatial analysis, crowdsourcing, data visualization, and simulation.
Digital humanities projects use such tools and methods as web-based media, digital archiving, data mining, geo-spatial analysis, crowdsourcing, data visualization, and simulation.
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