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＜E-Book＞
Event Attendance Prediction in Social Networks / by Xiaomei Zhang, Guohong Cao
(SpringerBriefs in Statistics. ISSN:21915458)

Edition	1st ed. 2021.
Publisher	(Cham : Springer International Publishing : Imprint: Springer)
Year	2021
Language	English
Size	VIII, 54 p. 22 illus., 14 illus. in color : online resource
Authors	*Zhang, Xiaomei author Cao, Guohong author SpringerLink (Online service)
Subjects	LCSH:Quantitative research LCSH:Data mining LCSH:Statistics  LCSH:Social sciences -- Statistical methods  All Subject Search FREE:Data Analysis and Big Data FREE:Data Mining and Knowledge Discovery FREE:Bayesian Inference FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Notes	Introduction -- Related Work -- Data Collection -- Event Attendance Prediction -- Performance Evaluations -- Conclusions and Future Research Directions This volume focuses on predicting users’ attendance at a future event at specific time and location based on their common interests. Event attendance prediction has attracted considerable attention because of its wide range of potential applications. By predicting event attendance, events that better fit users’ interests can be recommended, and personalized location-based or topic-based services related to the events can be provided to users. Moreover, it can help event organizers estimating the event scale, identifying conflicts, and help manage resources. This book first surveys existing techniques on event attendance prediction and other related topics in event-based social networks. It then introduces a context-aware data mining approach to predict the event attendance by learning how users are likely to attend future events. Specifically, three sets of context-aware attributes are identified by analyzing users’ past activities, including semantic, temporal, and spatial attributes. This book illustrates how these attributes can be applied for event attendance prediction by incorporating them into supervised learning models, and demonstrates their effectiveness through a real-world dataset collected from event-based social networks. HTTP:URL=https://doi.org/10.1007/978-3-030-89262-3

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