Mining Human Mobility in Location-Based Social Networks

Language: English

Published by Springer, Springer Mai 2015, 2015

3031007808 / 9783031007804

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This item is printed on demand - Print on Demand Titel. Neuware -In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to 'check in' at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., 'when and where a user (who) has been to for what,' corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to investigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 116 pp. Englisch.…

Seller Inventory # 9783031007804

Title
Mining Human Mobility in Location-Based Social Networks
Author
Huiji Gao
Publisher
Springer, Springer Mai 2015
Publication year
2015
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3031007808
ISBN 13
9783031007804
Item weight
233 grams
Dimensions
235x191x7 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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