Authors:
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 92)
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Table of contents (8 chapters)
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Front Matter
About this book
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.
Reviews
“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)
Authors and Affiliations
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Department of Informatics, University of Piraeus, Piraeus, Greece
Aristomenis S. Lampropoulos, George A. Tsihrintzis
Bibliographic Information
Book Title: Machine Learning Paradigms
Book Subtitle: Applications in Recommender Systems
Authors: Aristomenis S. Lampropoulos, George A. Tsihrintzis
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-19135-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-19134-8Published: 25 June 2015
Softcover ISBN: 978-3-319-38496-2Published: 17 October 2016
eBook ISBN: 978-3-319-19135-5Published: 13 June 2015
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XV, 125
Number of Illustrations: 26 b/w illustrations, 6 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Computer Imaging, Vision, Pattern Recognition and Graphics