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Titlebook: Event Attendance Prediction in Social Networks; Xiaomei Zhang,Guohong Cao Book 2021 The Author(s), under exclusive license to Springer Nat

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發(fā)表于 2025-3-21 17:00:24 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Event Attendance Prediction in Social Networks
編輯Xiaomei Zhang,Guohong Cao
視頻videohttp://file.papertrans.cn/318/317407/317407.mp4
概述Predicts event attendance with machine learning techniques.Provides a comprehensive guide for predicting event attendance using real data sets.Introduces a context-aware data mining approach to predic
叢書名稱SpringerBriefs in Statistics
圖書封面Titlebook: Event Attendance Prediction in Social Networks;  Xiaomei Zhang,Guohong Cao Book 2021 The Author(s), under exclusive license to Springer Nat
描述.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-w
出版日期Book 2021
關(guān)鍵詞data mining; mobile networks; supervised learning models; mobility prediction; event attendance predicti
版次1
doihttps://doi.org/10.1007/978-3-030-89262-3
isbn_softcover978-3-030-89261-6
isbn_ebook978-3-030-89262-3Series ISSN 2191-544X Series E-ISSN 2191-5458
issn_series 2191-544X
copyrightThe Author(s), under exclusive license to Springer Nature Switzerland AG 2021
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發(fā)表于 2025-3-21 21:14:36 | 只看該作者
Related Work,tion, the initial discussion centers around this topic. Existing works on short-term mobility prediction and long-term mobility prediction are reviewed. Then, we survey related work on event-based social networks, with focuses on recommendation systems and event attendance prediction.
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Event Attendance Prediction: Attributes,ds to predict the event attendance. This chapter focuses on identifying the context-aware attributes. The definition of context-aware attributes requires analysis of past events with similar topics. Therefore, we first present a semantic analysis method to calculate the semantic similarity between e
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Performance Evaluations,of the proposed solutions and evaluate how different parameters affect the performances. In this chapter, we first discuss the data selection, the experiment setting, and then present the evaluation results on the effectiveness of individual attributes and the performance of the three classifiers.
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978-3-030-89261-6The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
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