000			03586nam a22004095i 4500
999			_c103536 _d103536 _x1
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003			DE-He213
005			20230102113135.0
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007			cr nn 008mamaa
008			150613s2015 gw | s |||| 0|eng d
020			_a9783319191355
024	7		_a10.1007/978-3-319-19135-5 _2doi
040			_bspa _dES-MaUEC
050		4	_aQA76.9.I58 _b2015 EB
100	1		_aLampropoulos, Aristomenis S. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2017019733 _1http://viaf.org/viaf/192148752012241200564/
245	1	0	_aMachine Learning Paradigms _bApplications in Recommender Systems _cby Aristomenis S. Lampropoulos, George A. Tsihrintzis.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XV, 125 páginas 32 ilustraciones, 6 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aIntelligent Systems Reference Library, _x1868-4394 ; _v92
490	0		_aEngineering (Springer-11647)
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	3		_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.  .
988			_aEBSPRINGER_2018
650		7	_aAprendizaje automático _2embne _9166090
650		7	_aSistemas interactivos (Informática) _2embne _9139854
700	1		_aTsihrintzis, George A. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/nb2008014822 _1http://viaf.org/viaf/24132193/ _9100283
776	0	8	_iEdición impresa: _z9783319191362
776	0	8	_iEdición impresa: _z9783319191348
776	0	8	_iEdición impresa: _z9783319384962
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-19135-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI