Realtime Data Mining Self-Learning Techniques for Recommendation Engines /

• Main Authors: Paprotny, Alexander. (Author), Thess, Michael. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Birkh©Þuser, 2013.
• Series: Applied and Numerical Harmonic Analysis,
• Subjects: Mathematics.; Computer science > Mathematics.; Computer mathematics.; Computer software.; Computational Science and Engineering.; Mathematical Applications in Computer Science.; Mathematical Software.
• Online Access: View fulltext via EzAccess

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...

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.