Realtime Data Mining
Self-Learning Techniques for Recommendation Engines
Authors: Paprotny, Alexander, Thess, Michael
Free Preview- Specifically addresses recommendation engines from a mathematically rigorous viewpoint
- Discusses a control-theoretic framework for recommendation engines
- Provides applications to a number of areas within engineering and computer science
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- About this book
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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.
- Table of contents (14 chapters)
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Brave New Realtime World: Introduction
Pages 1-9
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Strange Recommendations? On the Weaknesses of Current Recommendation Engines
Pages 11-14
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Changing Not Just Analyzing: Control Theory and Reinforcement Learning
Pages 15-40
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Recommendations as a Game: Reinforcement Learning for Recommendation Engines
Pages 41-56
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How Engines Learn to Generate Recommendations: Adaptive Learning Algorithms
Pages 57-90
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Table of contents (14 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Realtime Data Mining
- Book Subtitle
- Self-Learning Techniques for Recommendation Engines
- Authors
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- Alexander Paprotny
- Michael Thess
- Series Title
- Applied and Numerical Harmonic Analysis
- Copyright
- 2013
- Publisher
- Birkhäuser Basel
- Copyright Holder
- Springer International Publishing Switzerland
- Distribution Rights
- Distribution rights for India: Researchco Book Centre, New Delhi, India
- eBook ISBN
- 978-3-319-01321-3
- DOI
- 10.1007/978-3-319-01321-3
- Hardcover ISBN
- 978-3-319-01320-6
- Softcover ISBN
- 978-3-319-34445-4
- Series ISSN
- 2296-5009
- Edition Number
- 1
- Number of Pages
- XXIII, 313
- Number of Illustrations
- 12 b/w illustrations, 88 illustrations in colour
- Topics
