With the development of GPS-enabled smart devices and wireless networks, spatial crowdsourcing has received wide attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, workers may show different preferences in different spatio-temporal contexts for the assigned tasks. It is a challenge to meet the spatio-temporal preferences of workers when assigning tasks. To this end, we propose a novel spatio-temporal preference-aware task assignment framework which consists of a translation-based recommendation phase and a task assignment phase. Specifically, in the first phase, we use a translation-based recommendation model to learn spatio-temporal effects from the workers’ historical task-performing activities and then calculate the spatio-temporal preference scores of workers. In the task assignment phase, we design a basic greedy algorithm and a Kuhn-Munkras (KM)-based algorithm which could achieve a better balance to maximize the total rewards and meet the spatio-temporal preferences of workers. Finally, extensive experiments are conducted, verifying the effectiveness and practicality of the proposed solutions.
CITATION STYLE
Zhu, C., Cui, Y., Zhao, Y., & Zheng, K. (2023). Task Assignment with Spatio-temporal Recommendation in Spatial Crowdsourcing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13421 LNCS, pp. 264–279). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-25158-0_21
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