Matrix and Tensor Factorization Techniques for Recommender Systems

This PDF book is become immediate popular in Computers genre. Matrix and Tensor Factorization Techniques for Recommender Systems is written by famous author Panagiotis Symeonidis and Ready to Download in ePUB, PDF or Kindle formats. Released by Springer in 2017-01-29. Click Download Book button to get book file and read directly from your devices. Here is a quick description and cover image of Matrix and Tensor Factorization Techniques for Recommender Systems book.

Matrix and Tensor Factorization Techniques for Recommender Systems
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Publisher : Springer
Release Date :
ISBN : 9783319413570
Pages : 102 pages
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Matrix and Tensor Factorization Techniques for Recommender Systems by Panagiotis Symeonidis Book PDF 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 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.


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