Matrix and Tensor Factorization Techniques for Recommender Systems

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  • Author : Panagiotis Symeonidis
  • Publisher : Springer
  • Pages : 102 pages
  • ISBN : 3319413570
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>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 page 102 pages. We cannot guarantee that Matrix and Tensor Factorization Techniques for Recommender Systems book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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

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

Low Rank Tensor Decomposition for Feature Extraction and Tensor Recovery

Low Rank Tensor Decomposition for Feature Extraction and Tensor Recovery
  • Author : Qiquan Shi
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK Low Rank Tensor Decomposition for Feature Extraction and Tensor Recovery

Feature extraction and tensor recovery problems are important yet challenging, particularly for multi-dimensional data with missing values and/or noise. Low-rank tensor decomposition approaches are widely used for solving these problems. This thesis focuses on three common tensor decompositions (CP, Tucker and t-SVD) and develops a set of decomposition-based approaches. The proposed methods aim to extract low-dimensional features from complete/incomplete data and recover tensors given partial and/or grossly corrupted observations.

Tensor Decomposition Meets Approximation Theory

Tensor Decomposition Meets Approximation Theory
  • Author : Ferre Knaepkens
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS 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 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

Spectral Learning on Matrices and Tensors

Spectral Learning on Matrices and Tensors
  • Author : Majid Janzamin,Rong Ge,Jean Kossaifi,Anima Anandkumar
  • Publisher : Unknown
  • Release : 25 November 2019
GET THIS 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 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

Higher order Kronecker Products and Tensor Decompositions

Higher order Kronecker Products and Tensor Decompositions
  • Author : Carla Dee Martin
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK Higher order Kronecker Products and Tensor Decompositions

The second problem in this dissertation involves solving shifted linear systems of the form (A - lambdaI) x = b when A is a Kronecker product of matrices. The Schur decomposition is used to reduce the shifted Kronecker product system to a Kronecker product of quasi-triangular matrices. The system is solved using a recursive block procedure which circumvents formation of the explicit product.

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 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 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

A Multilingual Exploration of Semantics in the Brain Using Tensor Decomposition

A Multilingual Exploration of Semantics in the Brain Using Tensor Decomposition
  • Author : Sharmistha Bardhan
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK A Multilingual Exploration of Semantics in the Brain Using Tensor Decomposition

The semantic concept processing mechanism of the brain shows that different neural activity patterns occur for different semantic categories. Multivariate Pattern Analysis of the brain fMRI data shows promising results in identifying active brain regions for a specific semantic category. Unsupervised learning technique such as tensor decomposition discovers the hidden structure from the brain data and proved to be useful as well. However, the existing methods are used for analyzing data from subjects who speak in one language and do

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : Qiang Yang,Zhi-Hua Zhou,Zhiguo Gong,Min-Ling Zhang,Sheng-Jun Huang
  • Publisher : Springer
  • Release : 20 May 2019
GET THIS BOOK Advances in Knowledge Discovery and Data Mining

The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in

Tensors

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

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

From Algebraic Structures to Tensors

From Algebraic Structures to Tensors
  • Author : Gérard Favier
  • Publisher : John Wiley & Sons
  • Release : 02 January 2020
GET THIS BOOK From Algebraic Structures to Tensors

Nowadays, tensors play a central role for the representation, mining, analysis, and fusion of multidimensional, multimodal, and heterogeneous big data in numerous fields. This set on Matrices and Tensors in Signal Processing aims at giving a self-contained and comprehensive presentation of various concepts and methods, starting from fundamental algebraic structures to advanced tensor-based applications, including recently developed tensor models and efficient algorithms for dimensionality reduction and parameter estimation. Although its title suggests an orientation towards signal processing, the results presented

Tensor Representation Techniques in Post Hartree Fock Methods Matrix Product State Tensor Format

Tensor Representation Techniques in Post Hartree Fock Methods  Matrix Product State Tensor Format
  • Author : Anonim
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK Tensor Representation Techniques in Post Hartree Fock Methods Matrix Product State Tensor Format

A approximation for post-Hartree Fock (HF) methods is presented applying tensor decomposition techniques in the matrix product state tensor format. In this ansatz, multidimensional tensors like integrals or wavefunction parameters are processed as an expansion of one-dimensional representing vectors. This approach has the potential to decrease the computational effort and the storage requirements of conventional algorithms drastically while allowing for rigorous truncation and error estimation.

Tensor multidimensional Array Decomposition Regression and Software for Statistics and Machine Learning

Tensor  multidimensional Array  Decomposition  Regression and Software for Statistics and Machine Learning
  • Author : James Yi-Wei Li
  • Publisher : Unknown
  • Release : 18 May 2021
GET THIS BOOK Tensor multidimensional Array Decomposition Regression and Software for Statistics and Machine Learning

This thesis illustrates connections between statistical models for tensors, introduces a novel linear model for tensors with 3 modes, and implements tensor software in the form of an R package. Tensors, or multidimensional arrays, are a natural generalization of the vectors and matrices that are ubiquitous in statistical modeling. However, while matrix algebra has been well-studied and plays a crucial role in the interaction between data and the parameters of any given model, algebra of higher-order arrays has been relatively overlooked