Matrix and Tensor Factorization Techniques for Recommender Systems

Download or Read online Matrix and Tensor Factorization Techniques for Recommender Systems full in PDF, ePub and kindle. This book written by Panagiotis Symeonidis and published by Springer which was released on 29 January 2017 with total pages 102. We cannot guarantee that Matrix and Tensor Factorization Techniques for Recommender Systems book is available in the library, click Get Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like.

Matrix and Tensor Factorization Techniques for Recommender Systems
Author :
Publisher : Springer
Release Date :
ISBN : 9783319413570
Pages : 102 pages
Rating : /5 ( users)
GET BOOK!

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.

Matrix and Tensor Factorization Techniques for Recommender Systems

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

GET BOOK!
Matrix and Tensor Factorization Techniques for Recommender Systems

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

GET BOOK!
Matrix and Tensor Decompositions in Signal Processing

The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief

GET BOOK!
Nonnegative Matrix and Tensor Factorizations

This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and

GET BOOK!
Algorithmic Aspects of Machine Learning

Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.

GET BOOK!
Spectral Learning on Matrices and Tensors

The authors of this monograph survey recent progress in using spectral methods including matrix and tensor decomposition techniques to learn many popular latent variable models. With careful implementation, tensor-based methods can run efficiently in practice, and in many cases they are the only algorithms with provable guarantees on running time

GET BOOK!
Nonnegative Matrix and Tensor Factorizations

This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and

GET BOOK!
Advances in Knowledge Discovery and Data Mining

The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 revised full papers and 58 revised short papers were carefully reviewed and selected from 331 submissions. The papers present new ideas, original

GET BOOK!
Matrix and Tensor Decomposition

Download or read online Matrix and Tensor Decomposition written by Christian Jutten, published by Unknown which was released on . Get Matrix and Tensor Decomposition Books now! Available in PDF, ePub and Kindle.

GET BOOK!
Tensor Methods in Statistics

This book provides a systematic development of tensor methods in statistics, beginning with the study of multivariate moments and cumulants. The effect on moment arrays and on cumulant arrays of making linear or affine transformations of the variables is studied. Because of their importance in statistical theory, invariant functions of

GET BOOK!
Scalable Low rank Matrix and Tensor Decomposition on Graphs

Mots-clés de l'auteur: Principal Component Analysis ; graphs ; low-rank and sparse decomposition ; clustering ; low-rank tensors.

GET BOOK!
Sketching as a Tool for Numerical Linear Algebra

Sketching as a Tool for Numerical Linear Algebra highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given a matrix, one first compressed it to a much smaller matrix by multiplying it by a (usually) random matrix with certain

GET BOOK!
Tensor Decomposition Meets Approximation Theory

This thesis studies three different subjects, namely tensors and tensor decomposition, sparse interpolation and Pad\'e or rational approximation theory. These problems find their origin in various fields within mathematics: on the one hand tensors originate from algebra and are of importance in computer science and knowledge technology, while on

GET BOOK!
Decomposability of Tensors

This book is a printed edition of the Special Issue "Decomposability of Tensors" that was published in Mathematics

GET BOOK!
Euro Par 2017  Parallel Processing

This book constitutes the proceedings of the 23rd International Conference on Parallel and Distributed Computing, Euro-Par 2017, held in Santiago de Compostela, Spain, in August/September 2017. The 50 revised full papers presented together with 2 abstract of invited talks and 1 invited paper were carefully reviewed and selected from 176 submissions. The papers are organized

GET BOOK!