國立虎尾科技大學 |

Event Attendance Prediction in Social Networks

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Event Attendance Prediction in Social Networks/ by Xiaomei Zhang, Guohong Cao.
作者:	Zhang, Xiaomei.
其他作者:	Cao, Guohong.
面頁冊數:	VIII, 54 p. 22 illus., 14 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics for Social Sciences, Humanities, Law. -
電子資源:	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. - 1st ed. 2021. - VIII, 54 p. 22 illus., 14 illus. in color.online resource. - SpringerBriefs in Statistics,2191-5458. - SpringerBriefs in Statistics,0.

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:

1211304
Statistics for Social Sciences, Humanities, Law.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Event Attendance Prediction in Social Networks
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