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

Feature Based Task Recommendation in Crowdsourcing with Implicit Observations