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Workers participating in a crowdsourcing platform can have a wide range ofabilities and interests. An important problem in crowdsourcing is the taskrecommendation problem, in which tasks that best match a particular worker'spreferences and reliabilities are recommended to that worker. A taskrecommendation scheme that assigns tasks more likely to be accepted by a workerwho is more likely to complete it reliably results in better performance forthe task requester. Without prior information about a worker, his preferencesand reliabilities need to be learned over time. In this paper, we propose amulti-armed bandit (MAB) framework to learn a worker's preferences and hisreliabilities for different categories of tasks. However, unlike the classicalMAB problem, the reward from the worker's completion of a task is unobservable.We therefore include the use of gold tasks (i.e., tasks whose solutions areknown \emph{a priori} and which do not produce any rewards) in our taskrecommendation procedure. Our model could be viewed as a new variant of MAB, inwhich the random rewards can only be observed at those time steps where goldtasks are used, and the accuracy of estimating the expected reward ofrecommending a task to a worker depends on the number of gold tasks used. Weshow that the optimal regret is $O(\sqrt{n})$, where $n$ is the number of tasksrecommended to the worker. We develop three task recommendation strategies todetermine the number of gold tasks for different task categories, and show thatthey are order optimal. Simulations verify the efficiency of our approaches.

Task Recommendation in Crowdsourcing Based on Learning Preferences and Reliabilities