Intelligent Systems Reference Library

Machine Learning Paradigms

Applications in Recommender Systems

Autoren: Lampropoulos, Aristomenis S., Tsihrintzis, George A.

Vorschau
  • Presents recent applications of Recommender Systems
  • Intended 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 emerging discipline of Recommender Systems and their applications
  • Explores the use of objective content-based features to model the individualized perception of similarity between multimedia data
Weitere Vorteile

Dieses Buch kaufen

eBook 78,10 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-19135-5
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: EPUB, PDF
  • Sofortiger eBook Download nach Kauf und auf allen Endgeräten nutzbar
  • Mengenrabatt verfügbar
Hardcover 128,39 €
Preis für Deutschland (Brutto)
Softcover 99,98 €
Preis für Deutschland (Brutto)
Über dieses Buch

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.

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“Researchers dealing with problems of accessing high volumes of complex data will make the best use of this book. Even though it is primarily a research text, the authors extensively present existing approaches to recommender systems and machine learning in a tutorial style. … I will recommend the book to my graduate students as a nice piece of research including well-presented background and good evaluation methodology.” (M. Bielikova, Computing Reviews, computingreviews.com, August, 2016)


Inhaltsverzeichnis (8 Kapitel)

Inhaltsverzeichnis (8 Kapitel)

Dieses Buch kaufen

eBook 78,10 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-19135-5
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: EPUB, PDF
  • Sofortiger eBook Download nach Kauf und auf allen Endgeräten nutzbar
  • Mengenrabatt verfügbar
Hardcover 128,39 €
Preis für Deutschland (Brutto)
Softcover 99,98 €
Preis für Deutschland (Brutto)
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Bibliografische Information

Bibliographic Information
Buchtitel
Machine Learning Paradigms
Buchuntertitel
Applications in Recommender Systems
Autoren
Titel der Buchreihe
Intelligent Systems Reference Library
Buchreihen Band
92
Copyright
2015
Verlag
Springer International Publishing
Copyright Inhaber
Springer International Publishing Switzerland
eBook ISBN
978-3-319-19135-5
DOI
10.1007/978-3-319-19135-5
Hardcover ISBN
978-3-319-19134-8
Softcover ISBN
978-3-319-38496-2
Buchreihen ISSN
1868-4394
Auflage
1
Seitenzahl
XV, 125
Anzahl der Bilder
26 schwarz-weiß Abbildungen, 6 Abbildungen in Farbe
Themen