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001 978-3-319-19135-5
003 DE-He213
005 20200421111156.0
007 cr nn 008mamaa
008 150613s2015 gw | s |||| 0|eng d
020 _a9783319191355
_9978-3-319-19135-5
024 7 _a10.1007/978-3-319-19135-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aLampropoulos, Aristomenis S.
_eauthor.
245 1 0 _aMachine Learning Paradigms
_h[electronic resource] :
_bApplications in Recommender Systems /
_cby Aristomenis S. Lampropoulos, George A. Tsihrintzis.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXV, 125 p. 32 illus., 6 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 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v92
505 0 _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.
520 _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.  .
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
700 1 _aTsihrintzis, George A.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319191348
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v92
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-19135-5
912 _aZDB-2-ENG
942 _cEBK
999 _c53511
_d53511