[Dbworld] New Book "Matrix and Tensor Factorization Techniques for Recommender Systems"

Symeonidis, Panagiotis; Zioupos, Andreas
Matrix and Tensor Factorization Techniques for Recommender Systems
2016, VI, 102 p. 29 illus., 22 illus. in color
Softcover, ISBN 978-3-319-41356-3
e-Book, ISBN 978-3-319-41357-0 


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. It 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.


Features and Benefits:
+ Covers all emerging tasks and cutting-edge techniques in matrix and tensor factorization for recommender systems 
+ Offers a rich blend of mathematical theory and practice for matrix and tensor decomposition, addressing seminal research ideas as well as practical issues
+ Includes a detailed experimental comparison of different factorization methods on real datasets, such as e.g. Epinions, GeoSocialRec, Last.fm, and BibSonomy

Keywords:
Recommender Systems – Information Retrieval – Factorization Methods – Machine Learning – Matrix Factorization

Read more detailed information 
(including detailed table of contents and sample chapter): 
http://www.springer.com/gp/book/9783319413563

Online version available under:
http://link.springer.com/book/10.1007%2F978-3-319-41357-0

ORDER INFORMATION:
Springer:  
http://www.springer.com/gp/book/9783319413563
Amazon:
https://www.amazon.com/Factorization-Techniques-Recommender-SpringerBriefs-Computer/dp/3319413562
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