Skill-based volunteering is an expanding branch of crowdsourcing where one may acquire sustainable services, solutions, and ideas from the crowd by connecting with them online. The optimal mapping between volunteers and tasks with collaboration becomes challenging for complex tasks demanding greater skills and cognitive ability. Unlike traditional crowdsourcing, volunteers like to work on their own schedule and locations. To address this problem, we propose a novel two-phase framework consisting of Initial Volunteer-Task Mapping (i-VTM) and Adaptive Common Slot Finding (a-CSF) algorithms. The i-VTM algorithm assigns volunteers to the tasks based on their skills and spatial proximity, whereas the a-CSF algorithm recommends appropriate common working time slots for successful volunteer collaboration. Both the algorithms aim to maximize the overall utility of the crowdsourcing platform. Experimenting with the UpWork dataset demonstrates the efficacy of our framework over existing state-of-the-art methods.


Computer Science

Keywords and Phrases

Collaboration; Common working time; Crowdsourcing; Skill-based volunteering; Spa-tial task allocation

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





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Publication Date

01 Jan 2022