Realtime Data Mining

Self-Learning Techniques for Recommendation Engines

  • Alexander Paprotny
  • Michael Thess
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Alexander Paprotny, Michael Thess
    Pages 1-9
  3. Alexander Paprotny, Michael Thess
    Pages 15-40
  4. Alexander Paprotny, Michael Thess
    Pages 91-118
  5. Alexander Paprotny, Michael Thess
    Pages 119-142
  6. Alexander Paprotny, Michael Thess
    Pages 143-181
  7. Alexander Paprotny, Michael Thess
    Pages 183-207
  8. Alexander Paprotny, Michael Thess
    Pages 209-225
  9. Alexander Paprotny, Michael Thess
    Pages 235-300
  10. Alexander Paprotny, Michael Thess
    Pages 301-304
  11. Alexander Paprotny, Michael Thess
    Pages E1-E10
  12. Back Matter
    Pages 305-313

About this book

Introduction

​​​​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.

Keywords

Markov decision process collaborative filtering hierarchical methods real-time analysis recommendation systems reinforcement learning

Authors and affiliations

  • Alexander Paprotny
    • 1
  • Michael Thess
    • 2
  1. 1.Research and Developmentprudsys AGBerlinGermany
  2. 2.Research and Developmentprudsys AGChemnitzGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-01321-3
  • Copyright Information Springer International Publishing Switzerland 2013
  • Publisher Name Birkhäuser, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-01320-6
  • Online ISBN 978-3-319-01321-3
  • Series Print ISSN 2296-5009
  • Series Online ISSN 2296-5017