000			04130nam a22004335i 4500
999			_c387294 _d387294
001			387294
003			ES-MaUEC
005			20230311191914.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2015 sz | s |||| 0|eng d
020			_a9783031019081
024	7		_a10.1007/978-3-031-01908-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.65 _b2015 EB
100	1		_aGao, Huiji _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687229
245	1	0	_aMining Human Mobility in Location-Based Social Networks _cby Huiji Gao, Huan Liu
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XVI, 99 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9431622 _aRedes sociales en Internet
650		7	_2embne _9442747 _aLocalización (Informática)
650		7	_2embne _9162648 _aData mining
700	1		_aLiu, Huan, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _949086 _d1958-
776	0	8	_iPrinted edition: _z9783031007804
776	0	8	_iPrinted edition: _z9783031030369
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01908-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI