Matrix Tensor Factorization Techniques by Symeonidis Panagiotis (19 results)
Language: English
Published by Springer, 2017
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Paperback. Condition: New. 1st ed. 2016.
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Published by Springer 2017-02-06, 2017
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Condition: New. pp. 108 1st ed. 2016 edition NO-PA16APR2015-KAP.
Language: English
Published by Springer International Publishing, 2017
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Published by Springer-Verlag New York Inc, 2017
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Language: English
Published by Springer, 2017
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Taschenbuch. Condition: Neu. Matrix and Tensor Factorization Techniques for Recommender Systems | Panagiotis Symeonidis (u. a.) | Taschenbuch | vi | Englisch | 2017 | Springer | EAN 9783319413563 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]c…om | Anbieter: preigu.
Language: English
Published by Springer, Berlin, Springer, 2017
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the algorithms used to provide recommendations by exploiting matrix factorization and tensor decomposition techniques. It highlights well-known decomposition methods for recommender systems, such as Singular Value Decomposition (…SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the pros and cons of each method for matrices and tensors. This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods. It also contains two chapters, where different matrix and tensor methods are compared experimentally on real data sets, such as Epinions, GeoSocialRec, Last.fm, BibSonomy, etc. and provides further insights into the advantages and disadvantages of each method. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in both recommenders and factorization methods. Lecturers can also use it for classes on data mining, recommender systems and dimensionality reduction methods.
Language: English
Published by Springer International Publishing AG, CH, 2017
Series: SpringerBriefs in Computer Science, Book 223 of 322. Book 223 of 322 - SpringerBriefs in Computer Science
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Paperback. Condition: New. 1st ed. 2016.
Language: English
Published by Springer-Verlag GmbH, 2017
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Language: English
Published by Springer-Verlag GmbH, 2017
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Softcover. Condition: gut. 2017. Matrix and Tensor Factorization Techniques for Recommender Systems In deutscher Sprache. pages.
Language: English
Published by Springer, 2017
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Condition: new. Questo è un articolo print on demand.
Language: English
Published by Springer, Berlin, Springer International Publishing, Springer, 2017
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents the algorithms used to provide recommendations by exploiting matrix factorization and tensor decomposition techniques. It highlights well-known decomposition methods for recommender systems, such as Singular Value… Decomposition (SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the pros and cons of each method for matrices and tensors. This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods. It also contains two chapters, where different matrix and tensor methods are compared experimentally on real data sets, such as Epinions, GeoSocialRec, Last.fm, BibSonomy, etc. and provides further insights into the advantages and disadvantages of each method. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in both recommenders and factorization methods. Lecturers can also use it for classes on data mining, recommender systems and dimensionality reduction methods. 102 pp. Englisch.
Language: English
Published by Springer, 2017
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- Softcover
- Print on Demand
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Condition: New. Print on Demand pp. 108.
Language: English
Published by Springer, 2017
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Condition: New. PRINT ON DEMAND pp. 108.
