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Matrix and Tensor Factorization Techniques for Recommender Systems

BuchKartoniert, Paperback
Verkaufsrang8443in
CHF77.00

Beschreibung

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.
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Details

ISBN/GTIN978-3-319-41356-3
ProduktartBuch
EinbandKartoniert, Paperback
VerlagSpringer
Erscheinungsdatum06.02.2017
Auflage1st ed. 2016
ReiheSpringerBriefs in Computer Science
Seiten102 Seiten
SpracheEnglisch
MasseBreite 155 mm, Höhe 235 mm
Gewicht1766 g
IllustrationenVI, 102 p. 51 illus., 22 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Artikel-Nr.6954731
KatalogBuchzentrum
Datenquelle-Nr.31950317
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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.

Schlagworte

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