  Existing research in crowdsourcing has investigated how to recommend tasks to
workers based on which task the workers have already completed, referred to as
{\em implicit feedback}. We, on the other hand, investigate the task
recommendation problem, where we leverage both implicit feedback and explicit
features of the task. We assume that we are given a set of workers, a set of
tasks, interactions (such as the number of times a worker has completed a
particular task), and the presence of explicit features of each task (such as,
task location). We intend to recommend tasks to the workers by exploiting the
implicit interactions, and the presence or absence of explicit features in the
tasks. We formalize the problem as an optimization problem, propose two
alternative problem formulations and respective solutions that exploit implicit
feedback, explicit features, as well as similarity between the tasks. We
compare the efficacy of our proposed solutions against multiple
state-of-the-art techniques using two large scale real world datasets.
