Realtime Data Mining Self-Learning Techniques for Recommendation Engines /
Paprotny, Alexander.
Realtime Data Mining Self-Learning Techniques for Recommendation Engines / [electronic resource] : by Alexander Paprotny, Michael Thess. - XXIII, 313 p. 100 illus. online resource. - Applied and Numerical Harmonic Analysis, 2296-5009 . - Applied and Numerical Harmonic Analysis, .
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.
ZDB-2-SMA
Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Enginesfeatures 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 ǣclassicǥ 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. Thismonograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinkingby 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.
9783319013213
10.1007/978-3-319-01321-3 doi
Mathematics.
Computer science.
Computer software.
Mathematics.
Computational Science and Engineering.
Mathematical Applications in Computer Science.
Mathematical Software.
QA71-90
004
Realtime Data Mining Self-Learning Techniques for Recommendation Engines / [electronic resource] : by Alexander Paprotny, Michael Thess. - XXIII, 313 p. 100 illus. online resource. - Applied and Numerical Harmonic Analysis, 2296-5009 . - Applied and Numerical Harmonic Analysis, .
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.
ZDB-2-SMA
Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Enginesfeatures 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 ǣclassicǥ 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. Thismonograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinkingby 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.
9783319013213
10.1007/978-3-319-01321-3 doi
Mathematics.
Computer science.
Computer software.
Mathematics.
Computational Science and Engineering.
Mathematical Applications in Computer Science.
Mathematical Software.
QA71-90
004