Utility-Aware Social Event-Participant Planning

She, Jieying; Tong, Yongxin; Chen, Lei

doi:10.1145/2723372.2749446

Cited by 81 publications

(34 citation statements)

References 33 publications

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“…In our experiments we use the largest Meetup dataset from [9], which contains data from California. Adopting the same approach as in [11]- [13], [15], in order to define the interest of a user to an event, we associate the events with the tags of the group who organize it. Then, we compute the likeness value using Jaccard similarity over the user-event tags.…”

Section: Experimental Analysis a Setupmentioning

confidence: 99%

Social Event Scheduling

Bikakis

Kalogeraki

Gunopulos

2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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A major challenge for social event organizers (e.g., event planning and marketing companies, venues) is attracting the maximum number of participants, since it has great impact on the success of the event, and, consequently, the expected gains (e.g., revenue, artist/brand publicity). In this paper, we introduce the Social Event Scheduling (SES) problem, which schedules a set of social events considering user preferences and behavior, events' spatiotemporal conflicts, and competing events, in order to maximize the overall number of attendees. We show that SES is strongly NP-hard, even in highly restricted instances. To cope with the hardness of the SES problem we design a greedy approximation algorithm. Finally, we evaluate our method experimentally using a dataset from the Meetup event-based social network.

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Section: Experimental Analysis a Setupmentioning

confidence: 99%

Social Event Scheduling

Bikakis

Kalogeraki

Gunopulos

2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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“…Kazemi and Shahabi [6] and To et al [10] aim to maximize the number of tasks that are assigned to workers. Furthermore, the conflict-aware spatial task assignment problems are studied [22][23][24]. Recently, the issue of online task assignment in dynamic spatial crowdsourcing scenarios is proposed [7].…”

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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“…Based on the original task assignment problem, both [24,25] study the conflict-aware task assignment problem, in which tasks may conflict with each other and thus cannot be assigned to the same worker. In addition, the work [26] not only considers spatiotemporal conflicts of tasks but also schedules the plan that each worker complete tasks [26]. Furthermore, Kazemi et al propose the quality-based task assignment problem [27], which utilizes majority voting techniques to guarantee the quality of task assignment results [28][29][30].…”

Section: Spatialmentioning

confidence: 99%

Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data

Qian

Zhu

et al. 2017

Journal of Sensors

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These days, with the increasingly widespread employment of sensors, particularly those attached to vehicles, the collection of spatial data is becoming easier and more accurate. As a result, many relevant areas, such as spatial crowdsourcing, are gaining ever more attention. A typical spatial crowdsourcing scenario involves an employer publishing a task and some workers helping to accomplish it. However, most of previous studies have only considered the spatial information of workers and tasks, while ignoring individual variations among workers. In this paper, we consider the Software Development Team Formation (SDTF) problem, which aims to assemble a team of workers whose abilities satisfy the requirements of the task. After showing that the problem is NP-hard, we propose three greedy algorithms and a multiple-phase algorithm to approximately solve the problem. Extensive experiments are conducted on synthetic and real datasets, and the results verify the effectiveness and efficiency of our algorithms.

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Utility-Aware Social Event-Participant Planning

Cited by 81 publications

References 33 publications

Social Event Scheduling

Social Event Scheduling

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data

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