Social Web Artifacts for Boosting Recommenders Theory and Implementa
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around t
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Cai-Nicolas Ziegler
Social Web Artifacts for Boosting Recommenders Theory and Implementation
13
Studies in Computational Intelligence Volume 487
Series Editor J. Kacprzyk, Warsaw, Poland
For further volumes: http://www.springer.com/series/7092
Cai-Nicolas Ziegler
Social Web Artifacts for Boosting Recommenders Theory and Implementation
ABC
PD Dr. Cai-Nicolas Ziegler PAYBACK GmbH (American Express) Albert-Ludwigs-Universität Freiburg i.Br. München Germany
ISSN 1860-949X ISSN 1860-9503 (electronic) ISBN 978-3-319-00526-3 ISBN 978-3-319-00527-0 (eBook) DOI 10.1007/978-3-319-00527-0 Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2013937342 c 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 accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
I first met Dr. Ziegler when he was a Ph.D. student spending a few months visiting our GroupLens Research lab in Minnesota. From the first I could tell he was a researcher of unusual vision, not content to work within the bounds of the previous literature on recommenders, but wanting to understand how the early recommender tools could be reshaped to meet needs that their users didn’t even imagine they had yet. He was particularly interested in understanding the fit
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