Realtime Data Mining Self-Learning Techniques for Recommendation Engines /
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. Furthermor...
Main Authors: | , |
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Corporate Author: | |
Format: | Electronic |
Language: | English |
Published: |
Cham :
Springer International Publishing : Imprint: Birkh©Þuser,
2013.
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Series: | Applied and Numerical Harmonic Analysis,
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Subjects: | |
Online Access: | View fulltext via EzAccess |
Table of Contents:
- 1 Brave New Realtime World Ớ<U+001c> Introduction
- 2 Strange Recommendations? Ớ<U+001c> On The Weaknesses Of Current Recommendation Engines
- 3 Changing Not Just Analyzing Ớ<U+001c> Control Theory And Reinforcement Learning
- 4 Recommendations As A Game Ớ<U+001c> Reinforcement Learning For Recommendation Engines
- 5 How Engines Learn To Generate Recommendations Ớ<U+001c> Adaptive Learning Algorithms
- 6 Up The Down Staircase Ớ<U+001c> Hierarchical Reinforcement Learning
- 7 Breaking Dimensions Ớ<U+001c> Adaptive Scoring With Sparse Grids
- 8 Decomposition In Transition - Adaptive Matrix Factorization
- 9 Decomposition In Transition Ii - Adaptive Tensor Factorization
- 10 The Big Picture Ớ<U+001c> 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 Ớ<U+001c> The Xelopes Library
- 13 Last Words Ớ<U+001c> Conclusion
- References
- Summary Of Notation.