000 | 03471nam a22005175i 4500 | ||
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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 |
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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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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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 |