Realtime Data Mining : Self-Learning Techniques for Recommendation Engines

種類:

電子ブック

責任表示:

by Alexander Paprotny, Michael Thess

出版情報:

Cham : Springer International Publishing : Imprint: Birkhäuser, 2013

著者名:

• Paprotny, Alexander
• Thess, Michael
• SpringerLink (Online service)

シリーズ名:

Applied and Numerical Harmonic Analysis ;

ISBN:

9783319013213 [3319013211]    

注記:

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, especi

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