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.
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