Kniha Realtime Data Mining Alexander Paprotny

Realtime Data Mining

Self-Learning Techniques for Recommendation Engines

Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: Birkhauser
Dostupnost: Skladem u dodavatele
Odesíláme za 5-8 dnů
2 286
Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learni...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2016
Stránek
313
EAN
9783319344454
ISBN
3319344455
Enbook ID
13937855
Vydavatel
Hmotnost
5153
Rozměry
155 x 235 x 19

Kompletní popis

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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