@inproceedings{88e9715c1ea14e0f827ae86e56aa9ceb,
title = "Task Assignment with Spatio-temporal Recommendation in Spatial Crowdsourcing",
abstract = "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{\textquoteright} 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.",
keywords = "Spatial crowdsourcing, Spatio-temporal preference, Task assignment",
author = "Chen Zhu and Yue Cui and Yan Zhao and Kai Zheng",
year = "2023",
doi = "10.1007/978-3-031-25158-0_21",
language = "English",
isbn = "978-3-031-25157-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "264--279",
editor = "Bohan Li and Chuanqi Tao and Lin Yue and Xuming Han and Diego Calvanese and Toshiyuki Amagasa",
booktitle = "Web and Big Data",
address = "Germany",
note = "6th International Joint Conference, APWeb-WAIM 2022 ; Conference date: 25-11-2022 Through 27-11-2022",
}