Abstract
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.
Recommended Citation
R. Samanta et al., "Volunteer Selection in Collaborative Crowdsourcing with Adaptive Common Working Time Slots," 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings, pp. 4643 - 4648, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.1109/GLOBECOM48099.2022.10001191
Department(s)
Computer Science
Keywords and Phrases
Collaboration; Common working time; Crowdsourcing; Skill-based volunteering; Spa-tial task allocation
International Standard Book Number (ISBN)
978-166543540-6
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2022
