Spatio-Temporal Recommendation in Social Media (Paperback, 1st ed. 2016)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	SpringerBriefs in Computer Science
Release date:	May 2016
First published:	2016
Authors:	Hongzhi Yin • Bin Cui
Dimensions:	235 x 155 x 7mm (L x W x T)
Format:	Paperback
Pages:	114
Edition:	1st ed. 2016
ISBN-13:	978-981-10-0747-7
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	981-10-0747-0
Barcode:	9789811007477

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