Oriented Online Route Recommendation for Spatial Crowdsourcing Task Workers

Liu, Yu; Yiu, Man Lung; Xu, Wenjian

doi:10.1007/978-3-319-22363-6_8

Cited by 52 publications

(32 citation statements)

References 21 publications

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“…A recent study distinguishes SC from related fields, including generic crowdsourcing, participatory sensing, volunteered geographic information, and online matching. Research efforts in SC have focused on different aspects, such as task assignment (e.g., [Kazemi and Shahabi 2012;Tong et al 2016;Liu et al 2016b;Hu et al 2016]), task scheduling (e.g., [Deng et al 2016;Li et al 2015;Sales Fonteles et al 2016]), quality control and trust (e.g., [Kazemi et al 2013;Cheng et al 2015]), privacy (e.g., Wang et al 2017]), incentive mechanism (e.g., [Gao et al 2015;Kandappu et al 2016;). The authors in [Kazemi and Shahabi 2012] proposed task assignment problem whose goal is to maximize the number of assigned tasks.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

Tran

Fan

et al. 2018

ACM Trans. Intell. Syst. Technol.

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Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as budget-per-time-period vs. budget-per-campaign and binary-utility vs. distance-based-utility. We study the hardness of the task assignment problem in the offline setting and propose online heuristics which exploits the spatial and temporal knowledge acquired over time. Our experiments are conducted with spatial crowdsourcing workloads generated by the SCAWG tool and extensive results show the effectiveness and efficiency of our proposed solutions.

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Section: Related Workmentioning

confidence: 99%

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

Tran

Fan

et al. 2018

ACM Trans. Intell. Syst. Technol.

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“…Kazemi et al [8] studies the route planning problem for a crowd worker and tries to maximize the number of completed tasks. The corresponding online version of [8] is studied in [25]. Wang et al [3] studies the entity resolution problem in crowdsourcing.…”

Section: Spatial Crowdsourcingmentioning

confidence: 99%

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

et al. 2017

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With the rapid development of mobile internet and online to offline marketing model, various spatial crowdsourcing platforms, such as Gigwalk and Gmission, are getting popular. Most existing studies assume that spatial crowdsourced tasks are simple and trivial. However, many real crowdsourced tasks are complex and need to be collaboratively finished by a team of crowd workers with different skills. Therefore, an important issue of spatial crowdsourcing platforms is to recommend some suitable teams of crowd workers to satisfy the requirements of skills in a task. In this paper, to address the issue, we first propose a more practical problem, called Top-k team recommendation in spatial crowdsourcing (TopkTR) problem. We prove that the TopkTR problem is NP-hard and designs a two-level-based framework, which includes an approximation algorithm with provable approximation ratio and an exact algorithm with pruning techniques to address it. In addition, we study a variant of the TopkTR problem, called TopkTRL, where a team leader is appointed among each recommended team of crowd workers in order to coordinate different crowd workers conveniently, and the aforementioned framework can be extended to address this variant. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

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“…Most existing research focuses on the task assignment (Deng, Shahabi, and Zhu 2015;Cheng et al 2015a;Alt et al 2010;Tong et al 2016a;2016b;Song et al 2017;Li, Yiu, and Xu 2015;Cheng et al 2017;Zhao et al 2017;Tong et al 2018a;2016b;2018b), which aims to maximize the total number of completed tasks (Kazemi and Shahabi 2012), the number of performed tasks for a worker with an optimal schedule (Deng, Shahabi, and Demiryurek 2013), or the diversity score of assignments (Cheng et al 2015b). An implicit assumption shared by these work is that the workers are willing to perform the tasks assigned to them.…”

Section: Introductionmentioning

confidence: 99%

Preference-Aware Task Assignment in Spatial Crowdsourcing

Zhao¹,

Xia²,

Liu

et al. 2019

AAAI

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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.

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Oriented Online Route Recommendation for Spatial Crowdsourcing Task Workers

Cited by 52 publications

References 21 publications

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

Preference-Aware Task Assignment in Spatial Crowdsourcing

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