LEADER 04243nam 22006735 450 001 9910299840703321 005 20200629193131.0 010 $a3-319-19135-7 024 7 $a10.1007/978-3-319-19135-5 035 $a(CKB)3710000000434159 035 $a(EBL)2120586 035 $a(OCoLC)911054496 035 $a(SSID)ssj0001525233 035 $a(PQKBManifestationID)11816148 035 $a(PQKBTitleCode)TC0001525233 035 $a(PQKBWorkID)11497121 035 $a(PQKB)10543614 035 $a(DE-He213)978-3-319-19135-5 035 $a(MiAaPQ)EBC2120586 035 $a(PPN)186401159 035 $a(EXLCZ)993710000000434159 100 $a20150613d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning Paradigms $eApplications in Recommender Systems /$fby Aristomenis S. Lampropoulos, George A. Tsihrintzis 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (135 p.) 225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v92 300 $aDescription based upon print version of record. 311 $a3-319-19134-9 320 $aIncludes bibliographical references. 327 $aIntroduction -- Review of Previous Work Related to Recommender Systems -- The Learning Problem.-Content Description of Multimedia Data -- Similarity Measures for Recommendations based on Objective Feature Subset Selection -- Cascade Recommendation Methods -- Evaluation of Cascade Recommendation Methods -- Conclusions and Future Work. 330 $aThis timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in ?big data? as well as ?sparse data? problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.  . 410 0$aIntelligent Systems Reference Library,$x1868-4394 ;$v92 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aOptical data processing 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 676 $a006.3 676 $a006.6 676 $a620 700 $aLampropoulos$b Aristomenis S$4aut$4http://id.loc.gov/vocabulary/relators/aut$0739886 702 $aTsihrintzis$b George A$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299840703321 996 $aMachine Learning Paradigms$92510296 997 $aUNINA