Task Recommendation in Reward-Based Crowdsourcing Systems

Crowdsourcing systems are distributed problem solving platforms, where small tasks are channelled to a crowd in the form of open calls for solutions. Reward based crowdsourcing systems tries to attract the interested and capable workers to provide solutions in return for monetary rewards. We study the task recommendation problem in reward based crowdsourcing platforms, where we leverage both implicit and explicit features of the worker-reward and worker-task attributes. Given a set of workers, set of tasks, participation, winner attributes, we intend to recommend tasks to workers by exploiting interactions between tasks and workers. Two models based on worker-reward based features and worker task based features are presented. The proposed approach is compared with multiple related techniques using real world dataset.