Name: Matrix and Tensor Factorization Techniques for Recommender Systems (Springerbriefs in Computer Science) (en Inglés)
Price: 84.08 USD
Availability: InStock
Author: Panagiotis Symeonidis; Andreas Zioupos
ISBN: 9783319413563

portada Matrix and Tensor Factorization Techniques for Recommender Systems (Springerbriefs in Computer Science) (en Inglés)

Matrix and Tensor Factorization Techniques for Recommender Systems (Springerbriefs in Computer Science) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Panagiotis Symeonidis; Andreas Zioupos

Año

2017

Idioma

Inglés

N° páginas

102

Encuadernación

Tapa Blanda

ISBN13

9783319413563

N° edición

Categorías

Inteligencia artificial

Matrix and Tensor Factorization Techniques for Recommender Systems (Springerbriefs in Computer Science) (en Inglés)

Panagiotis Symeonidis; Andreas Zioupos (Autor) · Springer · Tapa Blanda

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Reseña del libro "Matrix and Tensor Factorization Techniques for Recommender Systems (Springerbriefs in Computer Science) (en Inglés)"

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.