Overview
- Specifically addresses recommendation engines from a mathematically rigorous viewpoint
- Discusses a control-theoretic framework for recommendation engines
- Provides applications to a number of areas within engineering and computer science
Part of the book series: Applied and Numerical Harmonic Analysis (ANHA)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
About this book
​​​​Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data.​ The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's “classic” data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed.
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This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.
Authors and Affiliations
Bibliographic Information
Book Title: Realtime Data Mining
Book Subtitle: Self-Learning Techniques for Recommendation Engines
Authors: Alexander Paprotny, Michael Thess
Series Title: Applied and Numerical Harmonic Analysis
DOI: https://doi.org/10.1007/978-3-319-01321-3
Publisher: Birkhäuser Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2013
Hardcover ISBN: 978-3-319-01320-6Published: 16 December 2013
Softcover ISBN: 978-3-319-34445-4Published: 27 August 2016
eBook ISBN: 978-3-319-01321-3Published: 03 December 2013
Series ISSN: 2296-5009
Series E-ISSN: 2296-5017
Edition Number: 1
Number of Pages: XXIII, 313
Number of Illustrations: 12 b/w illustrations, 88 illustrations in colour
Topics: Computational Science and Engineering, Mathematical Applications in Computer Science, Mathematical Software