Abstract
AbstractWith the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computational Mechanics
Cited by
30 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Personalized Location-Preference Learning for Federated Task Assignment in Spatial Crowdsourcing;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21
2. Preference-Aware Group Task Assignment in Spatial Crowdsourcing: Effectiveness and Efficiency;IEEE Transactions on Knowledge and Data Engineering;2023-10-01
3. Worker-Churn-Based Task Assignment With Context-LSTM in Spatial Crowdsourcing;IEEE Transactions on Knowledge and Data Engineering;2023-09-01
4. Truthful incentive mechanism for multi-task assignment in crowdsouricng;Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023);2023-08-16
5. AdaTaskRec: An Adaptive Task Recommendation Framework in Spatial Crowdsourcing;ACM Transactions on Information Systems;2023-07-28