9783030892616 - Event Attendance Prediction in Social Networks (SpringerBriefs in Statistics) de Cao, Guohong; Zhang, Xiaomei (17 resultados)

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Condición: Neu. Event Attendance Prediction in Social Networks | Xiaomei Zhang (u. a.) | Taschenbuch | SpringerBriefs in Statistics | viii | Englisch | 2021 | Springer | EAN 9783030892616 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condición: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | 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.

Softcover. Condición: gut. 2021. Event Attendance Prediction in Social Networks In deutscher Sprache. pages.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 64 pp. Englisch.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This volume focuses on predicting users attendance at a future event at specific time and location based on their common interests. By predicting event attendance, events that better fit users interests can be recommended, and personalized location-based .

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 64 pp. Englisch.