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 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.

Produk Detail:

  • Author : Panagiotis Symeonidis
  • Publisher : Springer
  • Pages : 102 pages
  • ISBN : 3319413570
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMatrix and Tensor Factorization Techniques for Recommender Systems

Matrix and Tensor Factorization Techniques for Recommender Systems

Matrix and Tensor Factorization Techniques for Recommender Systems
  • Author : Panagiotis Symeonidis,Andreas Zioupos
  • Publisher : Springer
  • Release : 29 January 2017
GET THIS BOOKMatrix 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 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

Nonnegative Matrix and Tensor Factorizations

Nonnegative Matrix and Tensor Factorizations
  • Author : Andrzej Cichocki,Rafal Zdunek,Anh Huy Phan,Shun-ichi Amari
  • Publisher : John Wiley & Sons
  • Release : 10 July 2009
GET THIS BOOKNonnegative 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 data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF

Spectral Learning on Matrices and Tensors

Spectral Learning on Matrices and Tensors
  • Author : Majid Janzamin,Rong Ge,Jean Kossaifi,Anima Anandkumar
  • Publisher : Unknown Publisher
  • Release : 25 November 2019
GET THIS BOOKSpectral 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 and sample complexity. The focus is on a special type of tensor decomposition called CP decomposition, and the authors cover a wide range of algorithms to find the components of

Sketching as a Tool for Numerical Linear Algebra

Sketching as a Tool for Numerical Linear Algebra
  • Author : David P. Woodruff
  • Publisher : Now Publishers
  • Release : 14 November 2014
GET THIS BOOKSketching 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 properties. Much of the expensive computation can then be performed on the smaller matrix, thereby accelerating the solution for the original problem. It is an ideal primer for researchers and

Tensors in Image Processing and Computer Vision

Tensors in Image Processing and Computer Vision
  • Author : Santiago Aja-Fernández,Rodrigo de Luis Garcia,Dacheng Tao,Xuelong Li
  • Publisher : Springer Science & Business Media
  • Release : 21 May 2009
GET THIS BOOKTensors in Image Processing and Computer Vision

Tensor signal processing is an emerging field with important applications to computer vision and image processing. This book presents the state of the art in this new branch of signal processing, offering a great deal of research and discussions by leading experts in the area. The wide-ranging volume offers an overview into cutting-edge research into the newest tensor processing techniques and their application to different domains related to computer vision and image processing. This comprehensive text will prove to be

Nonnegative Matrix and Tensor Factorizations

Nonnegative Matrix and Tensor Factorizations
  • Author : Andrzej Cichocki,Rafal Zdunek,Anh Huy Phan,Shun-ichi Amari
  • Publisher : John Wiley & Sons
  • Release : 10 July 2009
GET THIS BOOKNonnegative 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 data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition
  • Author : Thierry Bouwmans,Necdet Serhat Aybat,El-hadi Zahzah
  • Publisher : CRC Press
  • Release : 20 September 2016
GET THIS BOOKHandbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition

Tensors

Tensors
  • Author : J. M. Landsberg
  • Publisher : American Mathematical Soc.
  • Release : 14 December 2011
GET THIS BOOKTensors

Tensors are ubiquitous in the sciences. The geometry of tensors is both a powerful tool for extracting information from data sets, and a beautiful subject in its own right. This book has three intended uses: a classroom textbook, a reference work for researchers in the sciences, and an account of classical and modern results in (aspects of) the theory that will be of interest to researchers in geometry. For classroom use, there is a modern introduction to multilinear algebra and

Tensor Network Contractions

Tensor Network Contractions
  • Author : Shi-Ju Ran
  • Publisher : Springer Nature
  • Release : 01 January 2020
GET THIS BOOKTensor Network Contractions

Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from

Tensor Decomposition Meets Approximation Theory

Tensor Decomposition Meets Approximation Theory
  • Author : Ferre Knaepkens
  • Publisher : Unknown Publisher
  • Release : 12 April 2021
GET THIS BOOKTensor 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 the other hand sparse interpolation and Pad\'e approximations stem from approximation theory. Although all three problems seem totally unrelated, they are deeply intertwined. The connection between them is exactly

Signal Processing in Neuroscience

Signal Processing in Neuroscience
  • Author : Xiaoli Li
  • Publisher : Springer
  • Release : 31 August 2016
GET THIS BOOKSignal Processing in Neuroscience

This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP. Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored