Matrix and Tensor Factorization Techniques for Recommender Systems (SpringerBriefs in Computer Science)

Symeonidis, Panagiotis

ISBN 10: 3319413562 / ISBN 13: 9783319413563

Editore: Springer, 2017

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Dati bibliografici

Titolo: Matrix and Tensor Factorization Techniques ...

Casa editrice: Springer

Data di pubblicazione: 2017

Legatura: Paperback

Condizione: New

Edizione: 1st ed. 2016.

Descrizione articolo

Riassunto:

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.

Recensione:

“This carefully written book offers advanced undergraduates, graduate students, researchers and professionals a comprehensive overview of the general concepts and techniques (e.g., models and algorithms) related to matrix and tensor factorization in the field of recommender systems, with a rich blend of theory and practice. ... I am definitely a recommender of this book!” (Bruno Carpentieri, Mathematical Reviews, August, 2017)