Realtime Data Mining Self-Learning Techniques for Recommendation Eng
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, tenso
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Alexander Paprotny Michael Thess
Realtime Data Mining Self-Learning Techniques for Recommendation Engines
Applied and Numerical Harmonic Analysis Series Editor John J. Benedetto University of Maryland College Park, MD, USA Editorial Advisory Board Akram Aldroubi Vanderbilt University Nashville, TN, USA
Gitta Kutyniok Technische Universita¨t Berlin Berlin, Germany
Douglas Cochran Arizona State University Phoenix, AZ, USA
Mauro Maggioni Duke University Durham, NC, USA
Hans G. Feichtinger University of Vienna Vienna, Austria
Zuowei Shen National University of Singapore Singapore, Singapore
Christopher Heil Georgia Institute of Technology Atlanta, GA, USA
Thomas Strohmer University of California Davis, CA, USA
Ste´phane Jaffard University of Paris XII Paris, France
Yang Wang Michigan State University East Lansing, MI, USA
Jelena Kovacˇevic´ Carnegie Mellon University Pittsburgh, PA, USA
For further volumes: http://www.springer.com/series/4968
Alexander Paprotny • Michael Thess
Realtime Data Mining Self-Learning Techniques for Recommendation Engines
Alexander Paprotny Research and Development prudsys AG Berlin, Germany
Michael Thess Research and Development prudsys AG Chemnitz, Germany
Corrected at 2nd printing 2014 ISSN 2296-5009 ISSN 2296-5017 (electronic) ISBN 978-3-319-01320-6 ISBN 978-3-319-01321-3 (eBook) DOI 10.1007/978-3-319-01321-3 Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2013953342 Mathematics Subject Classification (2010): 68T05, 68Q32, 90C40, 65C60, 62-07 © Springer International Publishing Switzerland 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and ac
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