Machine Learning Paradigms: Applications in Recommender Systems
Language: English
Published by Springer, 2015
Series: Book 64 of 188 - Intelligent Systems Reference Library
- Hardcover
- Used
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.ThriftBooks-Dallas
AbeBooks seller since July 2, 2009
Condition: Used - Very good
US$ 88.08
Quantity: 1 available
Add to basketItem description from seller
May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
Seller Inventory # G3319191349I4N00
- Title
- Machine Learning Paradigms: Applications in Recommender Systems
- Author
- Lampropoulos, Aristomenis S.; Tsihrintzis, George A.
- Publisher
- Springer
- Publication year
- 2015
- Condition
- Very Good
- Dust jacket
- No Jacket
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319191349
- ISBN 13
- 9783319191348
- Item weight
- 0.84 pounds
- Series
- Book 64 of 188: Intelligent Systems Reference Library
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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