Abstract
ABSTRACTThe crowdsourcing platform serves as an intermediary managing the interaction between a requester who posts a decomposable task and a pool of workers who bid to solve it. Each worker intending to take up the task (partially or fully) decomposes it into multiple independent subtasks and submits it to the platform. Selection of a diverse set of workers (based on the bids received) to solve the decomposable task is challenging as it requires balancing factors like cost and quality while encouraging collaboration. We propose a Worker Set Computation (WSC) methodology to address these challenges by selecting a custom set of potential workers who can collaboratively complete the task with the optimal cost, in an efficient way. The aging technique is employed to dynamically update the weight of each worker, giving more weightage to the feedback received in the recent past. This, in turn, not only favors those workers who were rated well in the immediate past but also ensures that one odd feedback does not influence the overall rating heavily. We compare the performance of the proposed method against the state‐of‐the‐art methods, considering the computational (and budget) requirements, as well as the aging‐based worker rating.
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