A Novel Social Event Organization Approach for Diverse User Choices

Huang, Jianbin; Zhou, Yu; Jia, Xiaolin

doi:10.1093/comjnl/bxw059

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…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.…”

Section: Experimental Analysis a Setupmentioning

confidence: 99%

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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%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Social Event Scheduling

Bikakis

Kalogeraki

Gunopulos

2018

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

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“…These works help social network platforms to improve their service qualities and bring great benefits to social users. The Social Event Organization (SEO) problem 8,9 aims to assign a given set of users to a given set of prescheduled events to maximize the overall social satisfaction. For a given event with a certain topic, physical location, and held time, Feng et al 10 discovered a set of influential event organizers who not only are experts on this event but also could influence the maximum number of people to attend this event.…”

Section: Introductionmentioning

confidence: 99%

Social event planning using hybrid pairwise Markov random fields

Xiao

Saidutta

Fekri

2021

Int J of Intelligent Sys

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Event‐based social networks (EBSNs) have become increasingly popular, which provide online social event management platforms for event organizers to publish and share social events (e.g., outdoor activities). In EBSNs, a major challenge for a social event organizer is how to plan a social event to attract the maximum number of attendance. To organize an event, three essential elements are required, namely, what (i.e., event content), where (i.e., event location), and when (i.e., event time). In this paper, we focus on the social event planning problem, which selects a location and time to hold a social event for the organizer with the given event content, to maximize the total number of participants. The solution of the social event planning problem could support decision‐making for social event organizers. For simplicity, we denote a location and time pair as an item in this paper. To solve the social event planning problem, we present a hybrid pairwise Markov random field (H‐PMRF) model which takes latent preferences of users, latent attributes of items, similarities between users and similarities between items into consideration. In particular, we construct an undirected graph where each node represents a user's decision on a specific item and each edge represents the relationship between the nodes, define the node potentials and edge potentials which model the dependency relationships between nodes, and give a joint probability distribution over the graph. Further, we adopt the Loopy Belief Propagation algorithm to compute the posterior probability distribution of each node in H‐PMRF and select the location and time to hold the event which could attract the maximum number of participants. We collect real‐world data set from DoubanEvent website and conduct extensive experiments on it. Experimental results show that the proposed model outperforms several baselines.

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“…In the setting of film industry, social-bookmarking, academic cooperation and crowdsourcing, teams are fundamental to these collaborative scenarios, where both the cost of a team and the ability of experts are essential and should be considered [ 2 , 5 , 6 ]. In addition, there are some other works related to team hiring problem such as top-k team formation [ 7 ], analytical team formation [ 8 ] and social event organization [ 9 – 12 ] and so on. Overall, the problem of hiring a team of experts for collaborative projects has extensive real-world applications and is an important problem to study.…”

Section: Introductionmentioning

confidence: 99%

Cost-minimizing team hires with participation constraint

et al. 2018

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Team formation, which aims to form a team to complete a given task by covering its required skills, furnishes a natural way to help organizers complete projects effectively. In this work, we propose a new team hiring problem. Given a set of projects with required skills, and a pool of experts , each of which has his own skillset, compensation demand and participation constraint (i.e., the maximum number of projects the expert can participate in simultaneously), we seek to hire a team of participation-constrained experts to complete all the projects so that the overall compensation is minimized. We refer to this as the participation constrained team hire problem. To the best of our knowledge, this is the first work to investigate the problem. We also study a special case of the problem, where the number of projects is within the participation constraint of each expert and design an exact algorithm for it. Since participation constrained team hire problem is proven to be NP-hard, we design three novel efficient approximate algorithms as its solution, each of which focuses on a particular perspective of the problem. We perform extensive experimental studies, on both synthetic and real datasets, to evaluate the performance of our algorithms. Experimental results show that our exact algorithm far surpasses the brute-force solutions and works well in practice. Besides, the three algorithms behave differently when distinct facets of the problem are involved.

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A Novel Social Event Organization Approach for Diverse User Choices

Cited by 8 publications

References 37 publications

Social Event Scheduling

Social Event Scheduling

Social event planning using hybrid pairwise Markov random fields

Cost-minimizing team hires with participation constraint

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