Authors:
- Covers all emerging tasks and cutting-edge techniques in matrix and tensor factorization for recommender systems
- Offers a rich blend of mathematical theory and practice for matrix and tensor decomposition, addressing seminal research ideas as well as practical issues
- Includes a detailed experimental comparison of different factorization methods on real datasets, such as e.g. Epinions, GeoSocialRec, Last.fm, and BibSonomy
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (8 chapters)
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Front Matter
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Matrix Factorization Techniques
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Front Matter
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Tensor Factorization Techniques
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Front Matter
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About this book
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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Authors and Affiliations
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Department of Informatics, Aristotle University of Thessalonik, Thessaloniki, Greece
Panagiotis Symeonidis
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Thessaloniki, Greece
Andreas Zioupos
About the authors
Andreas Zioupos has a B.Sc. degree in Mathematics and received his M.Sc. degree in Informatics & Management in 2015 from the Aristotle University of Thessaloniki, under the supervision of Dr. Panagiotis Symeonidis. He is an instructor for Google web tools and also has currently a contract as freelancer with the University of Piraeus on the project “Creating a framework for documentation, collection and disposal in the form of Linked Open Data from research results and official data of general government relating to domestic economic activity”. His research interests focus on data mining, recommender systems and dimensionality reduction methods.
Bibliographic Information
Book Title: Matrix and Tensor Factorization Techniques for Recommender Systems
Authors: Panagiotis Symeonidis, Andreas Zioupos
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-41357-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-3-319-41356-3Published: 06 February 2017
eBook ISBN: 978-3-319-41357-0Published: 29 January 2017
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: VI, 102
Number of Illustrations: 29 b/w illustrations, 22 illustrations in colour
Topics: Information Storage and Retrieval, Mathematical Applications in Computer Science, Mathematics of Computing, Artificial Intelligence