Fast Online Recommendation
Based on the spatiotemporal recommender models developed in the previous chapters, the top-k recommendation task can be reduced to an simple task of finding the top-k items with the maximum dot-products for the query/user vector over the set of item vecto
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Hongzhi Yin Bin Cui
Spatio-Temporal Recommendation in Social Media
123
SpringerBriefs in Computer Science Series editors Stan Zdonik, Brown University, Providence, USA Shashi Shekhar, University of Minnesota, Minneapolis, USA Jonathan Katz, University of Maryland, College Park, USA Xindong Wu, University of Vermont, Burlington, USA Lakhmi C. Jain, University of South Australia, Adelaide, Australia David Padua, University of Illinois Urbana-Champaign, Urbana, USA Xuemin (Sherman) Shen, University of Waterloo, Waterloo, Canada Borko Furht, Florida Atlantic University, Boca Raton, USA V.S. Subrahmanian, University of Maryland, College Park, USA Martial Hebert, Carnegie Mellon University, Pittsburgh, USA Katsushi Ikeuchi, University of Tokyo, Tokyo, Japan Bruno Siciliano, Università di Napoli Federico II, Napoli, Italy Sushil Jajodia, George Mason University, Fairfax, USA Newton Lee, Newton Lee Laboratories, LLC, Tujunga, USA
More information about this series at http://www.springer.com/series/10028
Hongzhi Yin Bin Cui •
Spatio-Temporal Recommendation in Social Media
123
Hongzhi Yin The University of Queensland Brisbane, QLD Australia
Bin Cui Peking University Beijing China
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-981-10-0747-7 ISBN 978-981-10-0748-4 (eBook) DOI 10.1007/978-981-10-0748-4 Library of Congress Control Number: 2016939068 © Springer Science+Business Media Singapore 2016 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 This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media Singapore Pte Ltd.
To all who make our lives worthwhile
Preface
In this book, we will guide you through the world of spatiotemporal recommendation in social media, which aims to help users find their potentially preferred items by mining the spatiotemporal data generated by the users in social media sites and apps. The spatiotemporal data imply extensive kno
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