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

Acerca del autor

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.

De la contraportada

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.