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9783319413563 - Matrix and Tensor Factorization Techniques for Recommender Systems Springerbriefs in Computer Science by Symeonidis, Panagiotis; Zioupos, Andreas (17 results)
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Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer International Publishing AG, CH, 2017
ISBN 10: 3319413562 ISBN 13: 9783319413563
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. 1st ed. 2016.
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Condition: As New. Unread book in perfect condition.
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Matrix and Tensor Factorization Techniques for Recommender Systems (SpringerBriefs in Computer Science)
Book 223 of 322: SpringerBriefs in Computer ScienceSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 59.14
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. pp. 108 1st ed. 2016 edition NO-PA16APR2015-KAP.
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Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer International Publishing, 2017
ISBN 10: 3319413562 ISBN 13: 9783319413563
Condition: New.
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Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer-Verlag New York Inc, 2017
ISBN 10: 3319413562 ISBN 13: 9783319413563
Paperback. Condition: Brand New. 108 pages. 9.00x6.00x0.25 inches. In Stock.
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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]com | Anbieter: preigu.
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Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer International Publishing AG, CH, 2017
ISBN 10: 3319413562 ISBN 13: 9783319413563
Paperback. Condition: New. 1st ed. 2016.
-
Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer, Berlin, Springer, 2017
ISBN 10: 3319413562 ISBN 13: 9783319413563
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.
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Condition: new. Questo è un articolo print on demand.
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Matrix and Tensor Factorization Techniques for Recommender Systems
Book 223 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer, Berlin, Springer International Publishing, Springer, 2017
ISBN 10: 3319413562 ISBN 13: 9783319413563
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
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.
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Condition: New. Print on Demand pp. 108.
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Condition: New. PRINT ON DEMAND pp. 108.
