Task Matching in Crowdsourcing

Crowd sourcing is evolving as a distributed problem-solving and business production model in recent years. In crowd sourcing paradigm, tasks are distributed to networked people to complete such that a company´s production cost can be greatly reduced. A crowd sourcing process involves operations of both requesters and workers. A requester submits a task request, a worker selects and completes a task, and the requester only pays the worker for the successful completion of the task. Obviously, it is not efficient that the amount of time spent on selecting a task is comparable with that spent on working on a task, but the monetary reward of a task is just a small amount. Literature mainly focused on exploring what type of tasks can be deployed to the crowd and analyzing the performance of crowd sourcing platforms. However, no existing work investigates on how to support workers to select tasks on crowd sourcing platforms easily and effectively. In this paper, we propose a novel idea on task matching in crowd sourcing to motivate workers to keep on working on crowd sourcing platforms in long run. The idea utilizes the past task preference and performance of a worker to produce a list of available tasks in the order of best matching with the worker during his task selection stage. It aims to increase the efficiency of task completion. We present some preliminary experimental results in case studies. Finally, we address the possible challenges and discuss the future directions.