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Spatio-Temporal Recommendation in Social Media [electronic resource] / by Hongzhi Yin, Bin Cui.

By: Yin, Hongzhi [author.].
Contributor(s): Cui, Bin [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: Singapore : Springer Singapore : Imprint: Springer, 2016Description: XIII, 114 p. 26 illus., 22 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811007484.Subject(s): Computer science | Database management | Data mining | Information storage and retrieval | Computer Science | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Information Systems Applications (incl. Internet) | Database ManagementAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
1. Introduction -- 2. Temporal Context-Aware Recommendation -- 3. Spatial Context-Aware Recommendation -- 4. Location-based and Real-time Recommendation -- 5. Fast Online Recommendation. .
In: Springer eBooksSummary: 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.
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1. Introduction -- 2. Temporal Context-Aware Recommendation -- 3. Spatial Context-Aware Recommendation -- 4. Location-based and Real-time Recommendation -- 5. Fast Online Recommendation. .

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.

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