000 04257nam a22005175i 4500
001 978-3-031-01908-1
003 DE-He213
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007 cr nn 008mamaa
008 220601s2015 sz | s |||| 0|eng d
020 _a9783031019081
_9978-3-031-01908-1
024 7 _a10.1007/978-3-031-01908-1
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aGao, Huiji.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986700
245 1 0 _aMining Human Mobility in Location-Based Social Networks
_h[electronic resource] /
_cby Huiji Gao, Huan Liu.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXVI, 99 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
505 0 _aAcknowledgments -- Figure Credits -- Introduction -- Analyzing LBSN Data -- Returning to Visited Locations -- Finding New Locations to Visit -- Epilogue -- Bibliography -- Authors' Biographies .
520 _aIn 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.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_986701
650 2 4 _aStatistics.
_914134
700 1 _aLiu, Huan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986703
710 2 _aSpringerLink (Online service)
_986705
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007804
776 0 8 _iPrinted edition:
_z9783031030369
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_986706
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01908-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c85994
_d85994