Feature Based Task Recommendation in Crowdsourcing with Implicit Observations
This work addresses task recommendation for crowdsourcing platforms, offering an incremental improvement by incorporating explicit features alongside implicit feedback.
The paper tackles the problem of recommending tasks to workers in crowdsourcing by leveraging both implicit feedback and explicit task features, proposing two optimization-based solutions that outperform multiple state-of-the-art techniques on two large-scale real-world datasets.
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.