Exploring the Impact of Dynamic Mutual Influence on Social Event Participation

Xu, Tong; Zhong, Hao; Zhu, Hengshu; Xiong, Hui; Chen, Enhong; Liu, Guannan

doi:10.1137/1.9781611974010.30

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…The iterative method can simulate the dynamic change process of user preferences. Xu et al [17] integrated the 7 Wireless Communications and Mobile Computing dynamic interdependence of potential event participants into the discrimination process of event attendance prediction through iterative methods.…”

Section: Event Attendance Prediction Methods In Ebsnmentioning

confidence: 99%

“…Xu et al [17] learned the user preference by modelling the user's personal preference and the interaction between user friends through the iterative method. The formula is as follows:…”

Section: User Preference Acquisitionmentioning

confidence: 99%

“…Precision [17,23,30,35,37,40,42] Accuracy [23,35,39,46] Recall [1,17,18,23,30,35,37,46] F-score [23,35,41,46] AUC [35,36,40,42,46] RMSE [22,47] Map [22,40] 10 Wireless Communications and Mobile Computing dynamically change over time. Therefore, how to obtain users' dynamic contextual preferences quickly also a research direction worthy of attention.…”

Section: Evaluation Metrics Referencesmentioning

confidence: 99%

“…Xu et al [17] carefully observed two types of potential participants, namely, event organizers ("seed users" with significant social influence) and new entrants. The study found that almost all groups face serious "cold start" problems.…”

Section: Cold Start Problemmentioning

confidence: 99%

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Research on the Prediction System of Event Attendance in an Event-Based Social Network

Lan

Chen

2022

Wireless Communications and Mobile Computing

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In recent years, an event-based social network recommendation system has attracted more and more researchers’ attention. Most EBSN recommendation systems mainly focus on recommending events to users. However, in many daily activities, it is necessary to accurately estimate the number of event participants for EBSN event organizers. As an effective means to solve the problem of event attendance prediction, the EBSN event attendance prediction system needs to mine the context information in EBSN fully and use the information to alleviate the problems of data sparsity and cold start. It brings some new challenges to the research of EBSN event attendance prediction systems. According to user characteristics and context factors, the main task of the EBSN event attendance prediction system is to obtain accurate user preferences, adopt efficient prediction algorithms to improve prediction performance, and avoid losses. This paper summarizes the research progress of the EBSN event attendance prediction system in recent years. Firstly, this paper analyzes the recent research on event attendance prediction in EBSN; secondly, we summarize the role, significance, and challenges of EBSN event attendance prediction; third, we summarize the critical technologies of EBSN event attendance prediction; the contents include mining the contextual information that affects the user’s participation in the event, user preference acquisition, the method of event attendance prediction, the data set of event attendance prediction, the evaluation indicators of event attendance prediction, etc.; fourth, we look forward to the future development directions of event attendance prediction from six aspects: the methods of integrating contextual factors, the user preference acquisition methods, the prediction algorithms, the utility evaluation of event attendance prediction, the user information security, and privacy protection, and the cold start issues; finally, we conclude this paper.

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Section: Event Attendance Prediction Methods In Ebsnmentioning

confidence: 99%

“…Xu et al [17] learned the user preference by modelling the user's personal preference and the interaction between user friends through the iterative method. The formula is as follows:…”

Section: User Preference Acquisitionmentioning

confidence: 99%

Section: Evaluation Metrics Referencesmentioning

confidence: 99%

Section: Cold Start Problemmentioning

confidence: 99%

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Research on the Prediction System of Event Attendance in an Event-Based Social Network

Lan

Chen

2022

Wireless Communications and Mobile Computing

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“…Event-based mining methods can be used to compute this value, e.g.,[1],[3],[4],[10],[16]-[18]. However, this is beyond the scope of this work.…”

mentioning

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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