Isbn: 9789811007477 - spatio-temporal recommendation in social media (16 risultati)

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Paperback or Softback. Condizione: New. Spatio-Temporal Recommendation in Social Media. Book.

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Condizione: New. pp. 128.

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Paperback. Condizione: Brand New. 128 pages. 9.00x6.00x0.25 inches. In Stock.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.…

Taschenbuch. Condizione: Neu. Spatio-Temporal Recommendation in Social Media | Hongzhi Yin (u. a.) | Taschenbuch | xiii | Englisch | 2016 | Springer | EAN 9789811007477 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students. 128 pp. Englisch.…

Condizione: New. Print on Demand pp. 128.

Condizione: New. PRINT ON DEMAND pp. 128.

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book to analyze the new characteristics of social media in the era of mobile internetCovers all emerging tasks and cutting-edge techniques for Spatio-Temporal Recommendation in Social MediaA.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users¿ behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users¿ behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 128 pp. Englisch.…