Overview
- 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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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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Keywords
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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Reviews
“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)
Authors and Affiliations
About the authors
Panagiotis Symeonidis is Adjunct Assistant Professor at the Aristotle University of Thessaloniki, Greece. He is the co-author of 2 international books, 18 journal papers, 4 book chapters and more than 28 articles in international conference proceedings. His articles have received almost 1400 citations from other scientific publications. He teaches courses on databases, data mining and data. For almost four years, he was the head of 1st EK (Laboratory Center) of Stavroupolis between September 2011 to July 2015. His research interests focus on recommender systems, social media in Web 2.0 and time-evolving online social networks.
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.
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 Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 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