Realtime Data Mining

Self-Learning Techniques for Recommendation Engines

Autor: Alexander Paprotny, Michael Thess

Język:

Oprawa: Miękka

Wydawca: Birkhauser

Dostępność: Dostępna u dostawcy

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Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learni...

Informacje o książce

Autor

Alexander Paprotny, Michael Thess

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2016

strony

313

EAN

9783319344454

ISBN

3319344455

Enbook ID

13937855

Wydawca

Birkhauser

Waga

5153

Wymiary

155 x 235 x 19

Pełny opis

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.

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Self-Learning Techniques for Recommendation Engines

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