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...

Full description

Bibliographic Details
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:
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.