Machine Learning Paradigms - Applications in Recommender Systems.

S. Lampropoulos, Aristomenis; Tsihrintzis, George A.

ISBN 10: 3319191349 / ISBN 13: 9783319191348

Published by Cham, Springer International Publishing., 2015

About this Item

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.

Seller Inventory # 7559JB

Bibliographic Details

• Title: Machine Learning Paradigms - Applications in Recommender Systems.
• Publisher: Cham, Springer International Publishing.
• Publication Date: 2015
• Language: English
• ISBN 10: 3319191349
• ISBN 13: 9783319191348
• Binding: Hardcover
• Seller catalogs:
  • Technik

About this title

Synopsis

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.