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Matrix and tensor factorization techniques for recommender systems

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Matrix and tensor factorization techniques for recommender systems/ by Panagiotis Symeonidis, Andreas Zioupos.
Author:	Symeonidis, Panagiotis.
other author:	Zioupos, Andreas.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	vi, 102 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Recommender systems (Information filtering) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-41357-0
ISBN:	9783319413570

Matrix and tensor factorization techniques for recommender systems
Symeonidis, Panagiotis.

Matrix and tensor factorization techniques for recommender systems[electronic resource] /by Panagiotis Symeonidis, Andreas Zioupos. - Cham :Springer International Publishing :2016. - vi, 102 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Part I Matrix Factorization Techniques -- 1. Introduction -- 2. Related Work on Matrix Factorization -- 3. Performing SVD on matrices and its Extensions -- 4. Experimental Evaluation on Matrix Decomposition Methods -- Part II Tensor Factorization Techniques -- 5. Related Work on Tensor Factorization -- 6. HOSVD on Tensors and its Extensions -- 7. Experimental Evaluation on Tensor Decomposition Methods -- 8 Conclusions and Future Work.

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.

ISBN: 9783319413570

Standard No.: 10.1007/978-3-319-41357-0doiSubjects--Topical Terms:

713827
Recommender systems (Information filtering)

LC Class. No.: QA76.9.I58 / S96 2016

Dewey Class. No.: 005.56

Matrix and tensor factorization techniques for recommender systems
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505 0 $a Part I Matrix Factorization Techniques -- 1. Introduction -- 2. Related Work on Matrix Factorization -- 3. Performing SVD on matrices and its Extensions -- 4. Experimental Evaluation on Matrix Decomposition Methods -- Part II Tensor Factorization Techniques -- 5. Related Work on Tensor Factorization -- 6. HOSVD on Tensors and its Extensions -- 7. Experimental Evaluation on Tensor Decomposition Methods -- 8 Conclusions and Future Work.
520 $a 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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