| 书目名称 | Event Attendance Prediction in Social Networks |
| 编辑 | Xiaomei Zhang,Guohong Cao |
| 视频video | |
| 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 |
| 丛书名称 | SpringerBriefs in Statistics |
| 图书封面 |  |
| 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 |
| 关键词 | data mining; mobile networks; supervised learning models; mobility prediction; event attendance predicti |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-89262-3 |
| isbn_softcover | 978-3-030-89261-6 |
| isbn_ebook | 978-3-030-89262-3Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 |
发表于 2025-3-21 17:00:24
