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Event attendance prediction in socia...

Zhang, Xiaomei.

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Event attendance prediction in social networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Event attendance prediction in social networks/ by Xiaomei Zhang, Guohong Cao.
Author:	Zhang, Xiaomei.
other author:	Cao, Guohong.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	viii, 54 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Related Work -- Data Collection -- Event Attendance Prediction -- Performance Evaluations -- Conclusions and Future Research Directions.
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-89262-3
ISBN:	9783030892623

Event attendance prediction in social networks
Zhang, Xiaomei.

Event attendance prediction in social networks[electronic resource] /by Xiaomei Zhang, Guohong Cao. - Cham :Springer International Publishing :2021. - viii, 54 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in statistics,2191-5458. - SpringerBriefs in statistics..

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.

ISBN: 9783030892623

Standard No.: 10.1007/978-3-030-89262-3doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / Z43 2021

Dewey Class. No.: 006.312

Event attendance prediction in social networks
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505 0 $a Introduction -- Related Work -- Data Collection -- Event Attendance Prediction -- Performance Evaluations -- Conclusions and Future Research Directions.
520 $a 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.
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