Social Network-Based Recommender Systems
This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative syste
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Social Network-Based Recommender Systems
Social Network-Based Recommender Systems
Daniel Schall
Social Network-Based Recommender Systems
123
Daniel Schall Siemens Corporate Technology Wien, Austria
ISBN 978-3-319-22734-4 DOI 10.1007/978-3-319-22735-1
ISBN 978-3-319-22735-1 (eBook)
Library of Congress Control Number: 2015951351 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 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. 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
To my son Kilian
Preface
People increasingly use social networks to manage various aspects of their lives such as communication, collaboration, and information sharing. A user’s network of friends may offer a wide range of important benefits such as receiving online help and support and the ability to exploit professional opportunities. One of the most profound properties of social networks is their dynamic nature governed by people constantly joining and leaving the social networks. The circle of friends may frequently change when people establish friendship through social links or when their interest in a social relationship ends and the link is removed. This book introduces novel techniques and algorithms for social network-based recommender systems. Here, concepts such as link prediction using graph patterns, following recommendation based on user authority, strategic partner selection in collaborative systems, and network formation based on “social brokers” are presented. In this book, well-established graph models such as the notion of hubs and authorities provide the basis for authority-based recommendation and are systematically extended towards a unified Hyperlink Induced Topic Search (HITS) and personalized PageRank model. Detailed experiments using various real-world datasets and systematic evaluation of recommendation results proof the app
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