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100   $a20130524d2013      u|             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aMultimedia Services in Intelligent Environments$b[electronic resource] $eAdvances in Recommender Systems /$fedited by George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain
205   $a1st ed. 2013.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2013.
215   $a1 online resource (178 p.)
225 1 $aSmart Innovation, Systems and Technologies,$x2190-3018 ;$v24
300   $aDescription based upon print version of record.
311   $a3-319-03340-9 
311   $a3-319-00371-2 
327   $aMultimedia Services in Intelligent Environments ? Advances in Recommender Systems -- A Survey of Approaches to Designing Recommender Systems -- Hybrid User Model for Capturing a User's Information Seeking Intent -- Recommender Systems: Network Approaches -- Toward the Next Generation of Recommender Systems: Applications and Research Challenges -- Pattern Extraction from Graphs and Beyond.
330   $aMultimedia services are now commonly used in various activities in the daily lives of humans. Related application areas include services that allow access to large depositories of information, digital libraries, e-learning and e-education, e-government and e-governance, e-commerce and e-auctions, e-entertainment, e-health and e-medicine, and e-legal services, as well as their mobile counterparts (i.e., m-services). Despite the tremendous growth of multimedia services over the recent years, there is an increasing demand for their further development. This demand is driven by the ever-increasing desire of society for easy accessibility to information in friendly, personalized and adaptive environments. In this book at hand, we examine recent Advances in Recommender Systems. Recommender systems are crucial in multimedia services, as they aim at protecting the service users from information overload. The book includes nine chapters, which present various recent research results in recommender systems. This research book is directed to professors, researchers, application engineers and students of all disciplines who are interested in learning more about recommender systems, advancing the corresponding state of the art and developing recommender systems for specific applications.  .
410  0$aSmart Innovation, Systems and Technologies,$x2190-3018 ;$v24
606   $aComputational intelligence
606   $aArtificial intelligence
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aComputational intelligence.
615  0$aArtificial intelligence.
615 14$aComputational Intelligence.
615 24$aArtificial Intelligence.
676   $a006.3
702   $aTsihrintzis$b George A$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aVirvou$b Maria$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aJain$b Lakhmi C$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910734097303321
996   $aMultimedia Services in Intelligent Environments$93400417
997   $aUNINA