  Workers participating in a crowdsourcing platform can have a wide range of
abilities and interests. An important problem in crowdsourcing is the task
recommendation problem, in which tasks that best match a particular worker's
preferences and reliabilities are recommended to that worker. A task
recommendation scheme that assigns tasks more likely to be accepted by a worker
who is more likely to complete it reliably results in better performance for
the task requester. Without prior information about a worker, his preferences
and reliabilities need to be learned over time. In this paper, we propose a
multi-armed bandit (MAB) framework to learn a worker's preferences and his
reliabilities for different categories of tasks. However, unlike the classical
MAB 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 are
known \emph{a priori} and which do not produce any rewards) in our task
recommendation procedure. Our model could be viewed as a new variant of MAB, in
which the random rewards can only be observed at those time steps where gold
tasks are used, and the accuracy of estimating the expected reward of
recommending a task to a worker depends on the number of gold tasks used. We
show that the optimal regret is $O(\sqrt{n})$, where $n$ is the number of tasks
recommended to the worker. We develop three task recommendation strategies to
determine the number of gold tasks for different task categories, and show that
they are order optimal. Simulations verify the efficiency of our approaches.
