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Machine Learning Paradigms: Applications in Recommender Systems: 92 (Intelligent Systems Reference Library) - Tapa dura
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialSpringer
- Año de publicación2015
- ISBN 10 3319191349
- ISBN 13 9783319191348
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas144
- Contacto del fabricanteno disponible
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Machine Learning Paradigms - Applications in Recommender Systems.
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Cham, Springer International Publishing., 2015
ISBN 10: 3319191349
ISBN 13: 9783319191348
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235 mm x 155 mm, 0 g. XV, 125 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Intelligent Systems Reference Library ; 92. Sprache: Englisch. Nº de ref. del artículo: 7559JB
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Machine Learning Paradigms - Applications in Recommender Systems
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Springer International Publishing, 2015
ISBN 10: 3319191349
ISBN 13: 9783319191348
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent applications of Recommender SystemsIntended for both the expert and researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader who wishes to learn more about the eme. Nº de ref. del artículo: 31548995
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Machine Learning Paradigms
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Springer International Publishing Jun 2015, 2015
ISBN 10: 3319191349
ISBN 13: 9783319191348
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 144 pp. Englisch. Nº de ref. del artículo: 9783319191348
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Machine Learning Paradigms : Applications in Recommender Systems
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Springer International Publishing, 2015
ISBN 10: 3319191349
ISBN 13: 9783319191348
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783319191348
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Machine Learning Paradigms: Applications in Recommender Systems (Intelligent Systems Reference Library, 92)
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Machine Learning Paradigms : Applications in Recommender Systems
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Machine Learning Paradigms: Applications in Recommender Systems (Intelligent Systems Reference Library, 92)
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Machine Learning Paradigms
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Springer International Publishing, Springer International Publishing Jun 2015, 2015
ISBN 10: 3319191349
ISBN 13: 9783319191348
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Buch. Condición: Neu. Neuware -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 datä as well as ¿sparse datä 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 144 pp. Englisch. Nº de ref. del artículo: 9783319191348
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Machine Learning Paradigms : Applications in Recommender Systems
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Machine Learning Paradigms: Applications in Recommender Systems
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Hardcover. Condición: Brand New. 9.25x6.50x0.75 inches. In Stock. Nº de ref. del artículo: x-3319191349
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