Task recommendation in crowdsourcing systems

Abstract

In crowdsourcing systems, tasks are distributed to networked people to complete such that a company's production cost can be greatly reduced. Obviously, it is not efficient that the amount of time for a worker 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. The available worker history makes it possible to mine workers' preference on tasks and to provide favorite recommendations. Our exploratory study on the survey results collected from Amazon Mechanical Turk (MTurk) shows that workers' histories can reflect workers' preferences on tasks in crowdsourcing systems. Task recommendation can help workers to find their right tasks faster as well as help requesters to receive good quality output quicker. However, previously proposed classification based task recommendation approach only considers worker performance history, but does not explore worker task searching history. In our paper, we propose a task recommendation framework for task preference modeling and preference-based task recommendation, aiming to recommend tasks to workers who are likely to prefer to work on and provide output that accepted by requesters. We consider both worker performance history and worker task searching history to reflect workers' task preference more accurately. To the best of our knowledge, we are the first to use matrix factorization for task recommendation in crowdsourcing systems. © 2012 ACM.