000 03471nam a22005175i 4500
001 978-1-4939-0286-6
003 DE-He213
005 20200421112222.0
007 cr nn 008mamaa
008 140208s2014 xxu| s |||| 0|eng d
020 _a9781493902866
_9978-1-4939-0286-6
024 7 _a10.1007/978-1-4939-0286-6
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aSymeonidis, Panagiotis.
_eauthor.
245 1 0 _aRecommender Systems for Location-based Social Networks
_h[electronic resource] /
_cby Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aV, 108 p. 41 illus., 33 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
505 0 _aIntroduction -- Recommender Systems -- Online Social Networks -- Location-based Social Networks -- Framework -- Algorithms -- Comparison -- Real Geo-social Recommender Systems -- Conclusions.
520 _aOnline social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of these recommender systems. Part 3 provides a step-by-step case study on the technical aspects of deploying and evaluating a real-world LBSN, which provides location, activity and friend recommendations. The material covered in the book is intended for graduate students, teachers, researchers, and practitioners in the areas of web data mining, information retrieval, and machine learning.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aInformation Systems Applications (incl. Internet).
700 1 _aNtempos, Dimitrios.
_eauthor.
700 1 _aManolopoulos, Yannis.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781493902859
830 0 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4939-0286-6
912 _aZDB-2-ENG
942 _cEBK
999 _c57450
_d57450