INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
9783319013213
NATIONAL BIBLIOGRAPHY NUMBER
Country Code
IR
Number
EN-52375
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
COUNTRY OF PUBLICATION OR PRODUCTlON
Country of publication
IR
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Realtime Data Minin
General Material Designation
[Book]
Other Title Information
:Self-Learning Techniques for Recommendation Engines
First Statement of Responsibility
/ by Alexander Paprotny, Michael Thess
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Cham
Name of Publisher, Distributor, etc.
: Springer International Publishing :Imprint: Birkh?nuser,
Date of Publication, Distribution, etc.
, 2013.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
XXIII, 313 p. 100 illus., 12 illus. in color., online resource.
SERIES
Series Title
(Applied and Numerical Harmonic Analysis,2296-5009)
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Electronic
CONTENTS NOTE
Text of Note
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.
Text of Note
1 Brave New Realtime World - Introduction -- 2 Strange Recommendations? - On The Weaknesses Of Current Recommendation Engines -- 3 Changing Not Just Analyzing - Control Theory And Reinforcement Learning -- 4 Recommendations As A Game - Reinforcement Learning For Recommendation Engines -- 5 How Engines Learn To Generate Recommendations - Adaptive Learning Algorithms -- 6 Up The Down Staircase - Hierarchical Reinforcement Learning -- 7 Breaking Dimensions - Adaptive Scoring With Sparse Grids -- 8 Decomposition In Transition - Adaptive Matrix Factorization -- 9 Decomposition In Transition Ii - Adaptive Tensor Factorization -- 10 The Big Picture - Towards A Synthesis Of Rl And Adaptive Tensor Factorization -- 11 What Cannot Be Measured Cannot Be Controlled - Gauging Success With A/B Tests -- 12 Building A Recommendation Engine - The Xelopes Library -- 13 Last Words - Conclusion -- References -- Summary Of Notation.?╗╣
SERIES
Title
Applied and Numerical Harmonic Analysis,2296-5009
TOPICAL NAME USED AS SUBJECT
Mathematics
Computer science
Computer software
Electronic books
LIBRARY OF CONGRESS CLASSIFICATION
Class number
E-BOOK
PERSONAL NAME - PRIMARY RESPONSIBILITY
Paprotny, Alexander.
PERSONAL NAME - SECONDARY RESPONSIBILITY
Thess, Michael
SpringerLink (Online service)
ORIGINATING SOURCE
Country
ایران
ELECTRONIC LOCATION AND ACCESS
Host name
9783319013206.pdf
Access number
عادی
Compression information
عادی
Date and Hour of Consultation and Access
9783319013206.pdf
Electronic Format Type
متن
old catalog
e
BL
1
a
Y
