04243nam 22006735 450 991029984070332120200629193131.03-319-19135-710.1007/978-3-319-19135-5(CKB)3710000000434159(EBL)2120586(OCoLC)911054496(SSID)ssj0001525233(PQKBManifestationID)11816148(PQKBTitleCode)TC0001525233(PQKBWorkID)11497121(PQKB)10543614(DE-He213)978-3-319-19135-5(MiAaPQ)EBC2120586(PPN)186401159(EXLCZ)99371000000043415920150613d2015 u| 0engur|n|---|||||txtccrMachine Learning Paradigms Applications in Recommender Systems /by Aristomenis S. Lampropoulos, George A. Tsihrintzis1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (135 p.)Intelligent Systems Reference Library,1868-4394 ;92Description based upon print version of record.3-319-19134-9 Includes bibliographical references.Introduction -- 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.This 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.  .Intelligent Systems Reference Library,1868-4394 ;92Computational intelligenceArtificial intelligenceOptical data processingComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computer Imaging, Vision, Pattern Recognition and Graphicshttps://scigraph.springernature.com/ontologies/product-market-codes/I22005Computational intelligence.Artificial intelligence.Optical data processing.Computational Intelligence.Artificial Intelligence.Computer Imaging, Vision, Pattern Recognition and Graphics.006.3006.6620Lampropoulos Aristomenis Sauthttp://id.loc.gov/vocabulary/relators/aut739886Tsihrintzis George Aauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299840703321Machine Learning Paradigms2510296UNINA