Matrix and Tensor Factorization Techniques for Recommender Systems

Written By Panagiotis Symeonidis
Matrix and Tensor Factorization Techniques for Recommender Systems
  • Publsiher : Springer
  • Release : 29 January 2017
  • ISBN : 3319413570
  • Pages : 102 pages
  • Rating : 4/5 from 21 reviews
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Read or download book entitled Matrix and Tensor Factorization Techniques for Recommender Systems written by Panagiotis Symeonidis which was release on 29 January 2017, this book published by Springer. Available in PDF, EPUB and Kindle Format. Book excerpt: 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 Date : 2017-01-29
  • Total pages : 102
  • ISBN : 3319413570
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Summary : 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 ...

Matrix and Tensor Decomposition

Matrix and Tensor Decomposition
  • Author : Christian Jutten
  • Publisher : Unknown
  • Release Date : 2021-04-18
  • Total pages : 212
  • ISBN : 3319413570
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Summary : Download or read online Matrix and Tensor Decomposition written by Christian Jutten, published by which was released on . Get Matrix and Tensor Decomposition Books now! Available in PDF, ePub and Kindle....

Matrix and Tensor Decompositions in Signal Processing

Matrix and Tensor Decompositions in Signal Processing
  • Author : Favier
  • Publisher : Wiley-Blackwell
  • Release Date : 2018-11-20
  • Total pages : 200
  • ISBN : 3319413570
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Summary : Download or read online Matrix and Tensor Decompositions in Signal Processing written by Favier, published by Wiley-Blackwell which was released on 2018-11-20. Get Matrix and Tensor Decompositions in Signal Processing Books now! Available in PDF, ePub and Kindle....

Tensor Decomposition Meets Approximation Theory

Tensor Decomposition Meets Approximation Theory
  • Author : Ferre Knaepkens
  • Publisher : Unknown
  • Release Date : 2017
  • Total pages : 212
  • ISBN : 3319413570
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Summary : 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 ...

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 Date : 2018
  • Total pages : 218
  • ISBN : 3319413570
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Summary : 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. ...

Spectral Learning on Matrices and Tensors

Spectral Learning on Matrices and Tensors
  • Author : Majid Janzamin,Rong Ge,Jean Kossaifi,Anima Anandkumar
  • Publisher : Unknown
  • Release Date : 2019-11-25
  • Total pages : 156
  • ISBN : 3319413570
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Summary : 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 ...

Higher order Kronecker Products and Tensor Decompositions

Higher order Kronecker Products and Tensor Decompositions
  • Author : Carla Dee Martin
  • Publisher : Unknown
  • Release Date : 2005
  • Total pages : 482
  • ISBN : 3319413570
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Summary : 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 ...

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 Date : 2009-07-10
  • Total pages : 500
  • ISBN : 3319413570
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Summary : 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 ...

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 Date : 2018
  • Total pages : 85
  • ISBN : 3319413570
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Summary : 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 ...

Urban Computing

Urban Computing
  • Author : Yu Zheng
  • Publisher : MIT Press
  • Release Date : 2019-02-05
  • Total pages : 632
  • ISBN : 3319413570
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Summary : An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications. Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban ...

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 Date : 2019-05-20
  • Total pages : 627
  • ISBN : 3319413570
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Summary : 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 ...

Robust Statistics for Signal Processing

Robust Statistics for Signal Processing
  • Author : Abdelhak M. Zoubir,Visa Koivunen,Esa Ollila,Michael Muma
  • Publisher : Cambridge University Press
  • Release Date : 2018-10-31
  • Total pages : 250
  • ISBN : 3319413570
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Summary : Understand the benefits of robust statistics for signal processing using this unique and authoritative text....

From Algebraic Structures to Tensors

From Algebraic Structures to Tensors
  • Author : Gérard Favier
  • Publisher : John Wiley & Sons
  • Release Date : 2020-01-02
  • Total pages : 318
  • ISBN : 3319413570
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Summary : 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 ...

Tensors

Tensors
  • Author : J. M. Landsberg
  • Publisher : American Mathematical Soc.
  • Release Date : 2011-12-14
  • Total pages : 439
  • ISBN : 3319413570
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Summary : 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 ...

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 Date : 2014
  • Total pages : 130
  • ISBN : 3319413570
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Summary : 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 ...