Matrix and Tensor Factorization Techniques for Recommender Systems
Language: English
Published by Springer 2017-02, 2017
Series: Book 223 of 322 - SpringerBriefs in Computer Science
- Softcover
- New
Seller: Chiron Media, Wallingford, United KingdomChiron Media
AbeBooks seller since August 2, 2010
Condition: New
£ 57.73
Quantity: 20 available
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Seller Inventory # 6666-IUK-9783319413563
- Title
- Matrix and Tensor Factorization Techniques for Recommender Systems
- Author
- Symeonidis, Panagiotis
- Publisher
- Springer 2017-02
- Publication year
- 2017
- Condition
- New
- Binding
- PF
- Language
- English
- ISBN 10
- 3319413562
- ISBN 13
- 9783319413563
- Series
- Book 223 of 322: SpringerBriefs in Computer Science
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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Chiron Media
Wallingford, United Kingdom
AbeBooks seller since August 2, 2010
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