Bottleneck-aware arrangement over event-based social networks: the max-min approach

Abstract

With the popularity of mobile computing and social media, various kinds of online event-based social network (EBSN) platforms, such as Meetup, Plancast and Whova, are gaining in prominence. A fundamental task of managing EBSN platforms is to recommend suitable social events to potential users according to the following three factors: spatial locations of events and users, attribute similarities between events and users, and friend relationships among users. However, none of the existing approaches considers all the aforementioned influential factors when they recommend users to proper events. Furthermore, the existing recommendation strategies neglect the bottleneck cases of the global recommendation. Thus, it is impossible for the existing recommendation solutions to be fair in real-world scenarios. In this paper, we first formally define the problem of bottleneck-aware social event arrangement (BSEA), which is proven to be NP-hard. To solve the BSEA problem approximately, Electronic supplementary material The online version of this article (World Wide Web we devise two greedy heuristic algorithms, Greedy and Random+Greedy, and a localsearch-based optimization technique. In particular, the Greedy algorithm is more effective but less efficient than the Random+Greedy algorithm in most cases. Moreover, a variant of the BSEA problem, called the Extended BSEA problem, is studied, and the above solutions can be extended to address this variant easily. Finally, we conduct extensive experiments on real and synthetic datasets which verify the efficiency and effectiveness of our proposed algorithms.

Social Event Scheduling

“…Parameters. Adopting the same setting as in the related works [6], [11]- [13], [15], we set the the default and maximum value of the of scheduled events k, to 100 and 500, respectively. Also, we vary the number of time intervals |T |, from k 5 up to 3k, with default value set to 3k 2 .…”

“…Note that, since the objective, the solution and the setting of our problem are substantially different (see Sect. I) from the related works [2], [5], [6], [11]- [15], the existing methods cannot be used to solve the SES problem. All algorithms were written in C++ and the experiments were performed on an 2.67GHz Intel Xeon E5640 with 32GB of RAM.…”

“…Recently, a number of works have been proposed in the context of event-participant planing [2], [5], [6], [11]- [15]. These works examine a problem from a different perspective: given a set of pre-scheduled events, they focus on finding the most appropriate assignments for the users (i.e., participants) attending the events.…”

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

“…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].…”

Finding Optimal Team for Multiskill Task Based on Vehicle Sensors Data

“…Recently, task assignment in real-time spatial crowdsourcing has also been studied by the online algorithmic model [12,23]. 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].…”