Matrix and Tensor Factorization Techniques for Recommender Systems

  • Panagiotis Symeonidis
  • Andreas Zioupos

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Matrix Factorization Techniques

    1. Front Matter
      Pages 1-1
    2. Panagiotis Symeonidis, Andreas Zioupos
      Pages 3-17
    3. Panagiotis Symeonidis, Andreas Zioupos
      Pages 19-31
    4. Panagiotis Symeonidis, Andreas Zioupos
      Pages 33-57
    5. Panagiotis Symeonidis, Andreas Zioupos
      Pages 59-65
  3. Tensor Factorization Techniques

    1. Front Matter
      Pages 67-67
    2. Panagiotis Symeonidis, Andreas Zioupos
      Pages 69-80
    3. Panagiotis Symeonidis, Andreas Zioupos
      Pages 81-93
    4. Panagiotis Symeonidis, Andreas Zioupos
      Pages 95-99
    5. Panagiotis Symeonidis, Andreas Zioupos
      Pages 101-102

About this book

Introduction

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.

Keywords

Recommender Systems Information Retrieval Factorization Methods Machine Learning Matrix Factorization

Authors and affiliations

  • Panagiotis Symeonidis
    • 1
  • Andreas Zioupos
    • 2
  1. 1.Department of InformaticsAristotle University of ThessalonikThessalonikiGreece
  2. 2.ThessalonikiGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-41357-0
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-41356-3
  • Online ISBN 978-3-319-41357-0
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • About this book
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