Understanding Event Organization at Scale in Event-Based Social Networks

Zhang, Jason Shuo; Lv, Qin

doi:10.1145/3243227

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…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%

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: Evaluation Metrics Referencesmentioning

confidence: 99%

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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“…2. Although existing works have extracted and exploited event specific semantic features such as sentiment, novelty and parts of speech tags from its description, they have proved to be ineffective in predicting the corresponding event's popularity (Zhang and Lv 2019). We believe that the lack of proper topical characterization of the events is the key factor behind it.…”

Section: Category Field Indicates the Broad Interest Of A Meetupmentioning

confidence: 99%

On the Role of Micro-categories to Characterize Event Popularity in Meetup

Bhowmick

Pramanik

Pathak

et al. 2021

ICWSM

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Event-based social networking platforms such as Meetup have recently witnessed a huge growth. However, with the rise in the volume of groups and events, making individual events attractive has become increasingly challenging for its organizers. As a result, we find that events hosted by groups of same category at similar venues and similar times, also widely differ in their popularity. Data study reveals that the topics specified in textual descriptions of events may be key to their popularity. In this paper, we introduce a novel concept of topical micro-categories in the context of EBSNs for accurately characterizing events, such that events belonging to the same micro-category exhibit similar popularity profile. We develop a principled method to detect such micro-categories from the textual descriptions of individual events. Our experiments reveal the significance of the detected micro-categories in determining the popularity of associated Meetup events and groups. We also investigate the effectiveness of the micro-categories in a real-world application scenario by developing a recommendation model; this model recommends relevant micro-categories to a group for hosting its future events with enhanced popularity. Notably, our model achieves an average NDCG score of around 0.75 showing a straight 5% improvement over the best performing competing method.

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“…Different types of recommendation problems were not only listed, but uniques and interesting characteristics of the networks were also analyzed in their work, such as information flows and locality structural groups. Various recommendation problems were studied in EBSNs [ 1 , 7 , 8 , 9 , 10 ]. However, the problem of activeness and loyalty in EBSNs still needs to be explored.…”

Section: Related Workmentioning

confidence: 99%

Activeness and Loyalty Analysis in Event-Based Social Networks

Trinh

Huang

et al. 2020

Entropy

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Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.

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Understanding Event Organization at Scale in Event-Based Social Networks

Cited by 12 publications

References 37 publications

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

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

On the Role of Micro-categories to Characterize Event Popularity in Meetup

Activeness and Loyalty Analysis in Event-Based Social Networks

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