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Realtime Data Mining
Alexander Paprotny, Michael Thess (Taschenbuch, Englisch)
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Beschreibung
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. 106,99 €
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Technische Daten
Erscheinungsdatum
27.08.2016
Sprache
Englisch
EAN
9783319344454
Herausgeber
Springer International Publishing
Serien- oder Bandtitel
Applied and Numerical Harmonic Analysis
Sonderedition
Nein
Autor
Alexander Paprotny, Michael Thess
Seitenanzahl
313
Einbandart
Taschenbuch
Buch Untertitel
Self-Learning Techniques for Recommendation Engines
Schlagwörter
Markov decision process, collaborative filtering, hierarchical methods, real-time analysis, recommendation systems, reinforcement learning
Thema-Inhalt
PBKS - Numerische Mathematik
UYA - Theoretische Informatik
UFM - Mathematische und statistische Software
Höhe
235 mm
Breite
15.5 cm
Transparenz & Sicherheit
Hersteller: Springer, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH
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