000			02491nam a22003255i 4500
001			65734
005			20240307063718.0
010			_a978-3-319-01321-3 _dcompra
090			_a65734
100			_a20150401d2013 k||y0pory50 ba
101			_aeng
102			_aDE
200			_aRealtime data mining _bDocumento electrónico _eself-learning techniques for recommendation engines _fAlexander Paprotny, Michael Thess
210			_aCham _cSpringer International Publishing _cBirkhäuser _d2013
215			_aXXIII, 313 p. _cil.
225			_aApplied and Numerical Harmonic Analysis
300			_aColocação: Online
303			_aDescribing 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.
606			_97514 _aRecolha de dados
606			_aProcessamento eletrónico de dados _94550
606			_9175 _aArmazenamento de dados
680			_aHF5548.2
700			_aPaprotny _bAlexander _947960
701			_aThess _bMichael _4070 _947962
801			_aPT _gRPC
856			_uhttp://dx.doi.org/10.1007/978-3-319-01321-3
942			_2lcc _cF _n0