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Applied and Numerical Harmonic Analysis

Realtime Data Mining

Self-Learning Techniques for Recommendation Engines

Authors: Paprotny, Alexander, Thess, Michael

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  • 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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eBook $84.99
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  • ISBN 978-3-319-01321-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
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Hardcover $139.99
price for USA in USD
Softcover $109.00
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: October 14, 2016
  • ISBN 978-3-319-34445-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Please be advised Covid-19 shipping restrictions apply. Please review prior to ordering
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About this book

​​​​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)

Table of contents (14 chapters)
  • Brave New Realtime World: Introduction

    Pages 1-9

    Paprotny, Alexander (et al.)

  • Strange Recommendations? On the Weaknesses of Current Recommendation Engines

    Pages 11-14

    Paprotny, Alexander (et al.)

  • Changing Not Just Analyzing: Control Theory and Reinforcement Learning

    Pages 15-40

    Paprotny, Alexander (et al.)

  • Recommendations as a Game: Reinforcement Learning for Recommendation Engines

    Pages 41-56

    Paprotny, Alexander (et al.)

  • How Engines Learn to Generate Recommendations: Adaptive Learning Algorithms

    Pages 57-90

    Paprotny, Alexander (et al.)

Buy this book

eBook $84.99
price for USA in USD
  • ISBN 978-3-319-01321-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.99
price for USA in USD
Softcover $109.00
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: October 14, 2016
  • ISBN 978-3-319-34445-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Please be advised Covid-19 shipping restrictions apply. Please review prior to ordering
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Realtime Data Mining
Book Subtitle
Self-Learning Techniques for Recommendation Engines
Authors
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