Author name cluster

Jinfu Xia

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2019 Conference Paper

Preference-Aware Task Assignment in Spatial Crowdsourcing

Yan Zhao
Jinfu Xia
Guanfeng Liu
Han Su
Defu Lian
Shuo Shang
Kai Zheng

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.

PDF Details

IJCAI Conference 2019 Conference Paper

Profit-driven Task Assignment in Spatial Crowdsourcing

Jinfu Xia
Yan Zhao
Guanfeng Liu
Jiajie Xu
Min Zhang
Kai Zheng

In Spatial Crowdsourcing (SC) systems, mobile users are enabled to perform spatio-temporal tasks by physically traveling to specified locations with the SC platforms. SC platforms manage the systems and recruit mobile users to contribute to the SC systems, whose commercial success depends on the profit attained from the task requesters. In order to maximize its profit, an SC platform needs an online management mechanism to assign the tasks to suitable workers. How to assign the tasks to workers more cost-effectively with the spatio-temporal constraints is one of the most difficult problems in SC. To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform. Specifically, we first establish a task reward pricing model with tasks' temporal constraints (i. e. , expected completion time and deadline). Then we adopt an optimal algorithm based on tree decomposition to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods.

PDF Details