Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approaches from four method families: (i) fa
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Denis Helic · Gerhard Leitner · Martin Stettinger · Alexander Felfernig · Zbigniew W. Ras´ (Eds.)
Foundations of Intelligent Systems 25th International Symposium, ISMIS 2020 Graz, Austria, September 23–25, 2020 Proceedings
123
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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More information about this series at http://www.springer.com/series/1244
Denis Helic Gerhard Leitner Martin Stettinger Alexander Felfernig Zbigniew W. Raś (Eds.) •
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Foundations of Intelligent Systems 25th International Symposium, ISMIS 2020 Graz, Austria, September 23–25, 2020 Proceedings
123
Editors Denis Helic Graz University of Technology Graz, Austria
Gerhard Leitner University of Klagenfurt Klagenfurt, Austria
Martin Stettinger Graz University of Technology Graz, Austria
Alexander Felfernig Graz University of Technology Graz, Austria
Zbigniew W. Raś University of North Carolina at Charlotte Charlotte, NC, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-59490-9 ISBN 978-3-030-59491-6 (eBook) https://doi.org/10.1007/978-3-030-59491-6 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer Nature Switzerland AG 2020 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, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This volume contains the papers selected for presentation at the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS 2020), which was held
