Matrix and Tensor Factorization Techniques for Recommender Systems

  • Author : Panagiotis Symeonidis
  • Publsiher : Springer
  • Release : 25 September 2016
  • ISBN : 3319413562
  • Page : 102 pages
  • Rating : 4/5 from 21 voters

Download or read online book entitled Matrix and Tensor Factorization Techniques for Recommender Systems written by Panagiotis Symeonidis and published by Springer. This book was released on 25 September 2016 with total page 102 pages. Available in PDF, EPUB and Kindle. Get best books that you want by click Get Book Button and Read as many books as you like. 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

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