東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Matrix and tensor factorization tech...

Symeonidis, Panagiotis.

Linked to FindBook

Google Book

Amazon

博客來

Matrix and tensor factorization techniques for recommender systems

Record Type:	Electronic resources : 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.
[NT 15003449]:	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.
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:

1002434
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
LDR:02753nmm a2200337 a 4500 001 2084053
003 DE-He213
005 20170130113558.0
006 m d
007 cr nn 008maaau
008 170820s2016 gw s 0 eng d
020 $a 9783319413570 $q (electronic bk.)
020 $a 9783319413563 $q (paper)
024 7 $a 10.1007/978-3-319-41357-0 $2 doi
035 $a 978-3-319-41357-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.I58 $b S96 2016
072 7 $a UNH $2 bicssc
072 7 $a UND $2 bicssc
072 7 $a COM030000 $2 bisacsh
082 0 4 $a 005.56 $2 23
090 $a QA76.9.I58 $b S986 2016
100 1 $a Symeonidis, Panagiotis. $3 2058741
245 1 0 $a Matrix and tensor factorization techniques for recommender systems $h [electronic resource] / $c by Panagiotis Symeonidis, Andreas Zioupos.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a vi, 102 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
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.
650 0 $a Recommender systems (Information filtering) $3 1002434
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a Mathematical Applications in Computer Science. $3 1567978
650 2 4 $a Mathematics of Computing. $3 891213
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
700 1 $a Zioupos, Andreas. $3 3209798
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in computer science. $3 1567571
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-41357-0
950 $a Computer Science (Springer-11645)