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Mineria de dades --- Comunitats virtuals --- Context-aware computing. --- Data mining. --- CV --- Comunitat virtual --- Comunitats electròniques --- Grups en internet --- Informàtica comunitaria --- Telemàtica comunitaria --- Xarxes socials (Informàtica) --- Xarxes socials en línia --- Xarxes socials per internet --- Xarxes telemàtiques --- Telemàtica --- Xarxes d'ordinadors --- Xarxes socials --- Mitjans socials --- Comunitats de construcció del coneixement --- Campus virtuals --- Cultura participativa --- Marcadors socials --- Influenciadors (Internet) --- Web 2.0 --- Prospecció de dades --- Cerca en bases de dades --- Arquitectura orientada a serveis (Informàtica) --- Extracció de dades de llocs web --- Mineria de web --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Context-aware pervasive systems --- Context-awareness (Computer science) --- Ubiquitous computing --- Global system for mobile communications --- Mobile computing --- Sensor networks
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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. .
Statistical science --- Mathematical statistics --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- datamining --- statistiek --- data acquisition
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Mineria de dades --- Comunitats virtuals --- CV --- Comunitat virtual --- Comunitats electròniques --- Grups en internet --- Informàtica comunitaria --- Telemàtica comunitaria --- Xarxes socials (Informàtica) --- Xarxes socials en línia --- Xarxes socials per internet --- Xarxes telemàtiques --- Telemàtica --- Xarxes d'ordinadors --- Xarxes socials --- Mitjans socials --- Comunitats de construcció del coneixement --- Campus virtuals --- Cultura participativa --- Marcadors socials --- Web 2.0 --- Prospecció de dades --- Cerca en bases de dades --- Arquitectura orientada a serveis (Informàtica) --- Extracció de dades de llocs web --- Mineria de web --- Influenciadors --- Influenciadors (Internet) --- Context-aware computing. --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Context-aware pervasive systems --- Context-awareness (Computer science) --- Ubiquitous computing --- Global system for mobile communications --- Mobile computing --- Sensor networks
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