Realtime Data Mining

Self-Learning Techniques for Recommendation Engines

Nonfiction, Science & Nature, Mathematics, Applied, Computers, Advanced Computing, Computer Science, Science
Cover of the book Realtime Data Mining by Alexander Paprotny, Michael Thess, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Alexander Paprotny, Michael Thess ISBN: 9783319013213
Publisher: Springer International Publishing Publication: December 3, 2013
Imprint: Birkhäuser Language: English
Author: Alexander Paprotny, Michael Thess
ISBN: 9783319013213
Publisher: Springer International Publishing
Publication: December 3, 2013
Imprint: Birkhäuser
Language: English

​​​​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.

 

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

​​​​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.

 

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.

More books from Springer International Publishing

Cover of the book Amorphous Drugs by Alexander Paprotny, Michael Thess
Cover of the book Teaching Ethics with Three Philosophical Novels by Alexander Paprotny, Michael Thess
Cover of the book Analysis and Geometry in Control Theory and its Applications by Alexander Paprotny, Michael Thess
Cover of the book Paraconsistent Logic: Consistency, Contradiction and Negation by Alexander Paprotny, Michael Thess
Cover of the book Archaeological Landscape Evolution by Alexander Paprotny, Michael Thess
Cover of the book Poetry and Pedagogy across the Lifespan by Alexander Paprotny, Michael Thess
Cover of the book Structural Dynamics of HIV by Alexander Paprotny, Michael Thess
Cover of the book Software Engineering Research, Management and Applications by Alexander Paprotny, Michael Thess
Cover of the book Galileo and the Equations of Motion by Alexander Paprotny, Michael Thess
Cover of the book Mathematical Sciences with Multidisciplinary Applications by Alexander Paprotny, Michael Thess
Cover of the book Crohn’s Disease by Alexander Paprotny, Michael Thess
Cover of the book Novel Measurement and Assessment Tools for Monitoring and Management of Land and Water Resources in Agricultural Landscapes of Central Asia by Alexander Paprotny, Michael Thess
Cover of the book Tumor Deposits by Alexander Paprotny, Michael Thess
Cover of the book The Interaction Between Local and International Peacebuilding Actors by Alexander Paprotny, Michael Thess
Cover of the book Big Data Applications and Use Cases by Alexander Paprotny, Michael Thess
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy