Abstract
With the fast-paced development of mobile networks and the widespread usage of mobile devices, Spatial Crowdsourcing (SC) has drawn increasing attention in recent years. SC has the potential for collecting information for a broad range of applications such as on-demand local delivery and on-demand transportation. One of the critical issues in SC is task assignment that allocates location-based tasks (e.g., delivering food and packages) to appropriate moving workers (i.e., intelligent device carriers). In this paper, we study a loyalty-based task assignment problem, which aims to maximize the overall rewards of workers while considering worker loyalty. We propose a two-phase framework to solve the problem, including a worker loyalty prediction and a task assignment phase. In the first phase, we use a model based on an efficient time series prediction method called Prophet and an Entropy Weighting method to extract workers' short-term and long-term loyalty and then predict workers' current loyalty scores. In the task assignment phase, we design a Kuhn-Munkras-based algorithm that achieves the optimal task assignment and an efficient Degree-Reduction-based algorithm with minority first scheme. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.
Originalsprog | Engelsk |
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Titel | CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management |
Forlag | Association for Computing Machinery |
Publikationsdato | 2022 |
Sider | 1014–1023 |
ISBN (Elektronisk) | 978-1-4503-9236-5 |
DOI | |
Status | Udgivet - 2022 |
Begivenhed | 31st ACM International Conference on Information and Knowledge Management - Atlanta, USA Varighed: 17 okt. 2022 → 21 okt. 2022 |
Konference
Konference | 31st ACM International Conference on Information and Knowledge Management |
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Land/Område | USA |
By | Atlanta |
Periode | 17/10/2022 → 21/10/2022 |