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

1st ed. 2021.
出版者 (Cham : Springer International Publishing : Imprint: Springer)
出版年 2021
本文言語 英語
大きさ VIII, 54 p. 22 illus., 14 illus. in color : online resource
著者標目 *Zhang, Xiaomei author
Cao, Guohong author
SpringerLink (Online service)
件 名 LCSH:Quantitative research
LCSH:Data mining
LCSH:Statistics 
LCSH:Social sciences -- Statistical methods  全ての件名で検索
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
一般注記 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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Springer eBooks 9783030892623
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データ種別 電子ブック
分 類 LCC:QA76.9.Q36
DC23:1,422
DC23:005.7
書誌ID 4000141938
ISBN 9783030892623

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