Matrix And Tensor Factorization Techniques For Recommender Systems by Panagiotis SymeonidisMatrix And Tensor Factorization Techniques For Recommender Systems by Panagiotis Symeonidis

Matrix And Tensor Factorization Techniques For Recommender Systems

byPanagiotis Symeonidis, Andreas Zioupos

Paperback | February 6, 2017

Pricing and Purchase Info

$81.74 online 
$96.95 list price save 15%
Earn 409 plum® points

Prices and offers may vary in store

Quantity:

In stock online

Ships free on orders over $25

Not available in stores

about

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.

Panagiotis Symeonidis is Adjunct Assistant Professor at the Aristotle University of Thessaloniki, Greece. He is the co-author of 2 international books, 18 journal papers, 4 book chapters and more than 28 articles in international conference proceedings. His articles have received almost 1400 citations from other scientific publications...
Loading
Title:Matrix And Tensor Factorization Techniques For Recommender SystemsFormat:PaperbackDimensions:102 pages, 23.5 × 15.5 × 0.02 inPublished:February 6, 2017Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319413562

ISBN - 13:9783319413563

Reviews

Table of Contents

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.

Editorial Reviews

"This carefully written book offers advanced undergraduates, graduate students, researchers and professionals a comprehensive overview of the general concepts and techniques (e.g., models and algorithms) related to matrix and tensor factorization in the field of recommender systems, with a rich blend of theory and practice. . I am definitely a recommender of this book!" (Bruno Carpentieri, Mathematical Reviews, August, 2017)