TY - GEN
T1 - Preference-aware task assignment in spatial crowdsourcing
AU - Zhao, Yan
AU - Xia, Jinfu
AU - Liu, Guanfeng
AU - Su, Han
AU - Lian, Defu
AU - Shang, Shuo
AU - Zheng, Kai
PY - 2019
Y1 - 2019
N2 - With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers' temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers' temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.
AB - With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers' temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers' temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.
UR - http://www.scopus.com/inward/record.url?scp=85078948051&partnerID=8YFLogxK
U2 - 10.1609/aaai.v33i01.33012629
DO - 10.1609/aaai.v33i01.33012629
M3 - Conference proceeding contribution
SN - 9781577358091
T3 - AAAI Conference on Artificial Intelligence
SP - 2629
EP - 2636
BT - The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
PB - Association for the Advancement of Artificial Intelligence
CY - Palo Alto, CA
T2 - 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence
Y2 - 27 January 2019 through 1 February 2019
ER -