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- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)
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Table of contents (8 chapters)
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Front Matter
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Foundations
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Front Matter
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Recommendation Techniques for Social Tagging Systems
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Front Matter
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Implementing Recommender Systems for Social Tagging
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Front Matter
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About this book
Reviews
From the reviews:
“The book is a useful contribution towards harnessing crowd sourced descriptions by using recommender systems as it epitomises the long experience of the majority of the authors in social tagging systems … . the engaged researcher in the area will benefit from reading this book in that it provides a good orientation over state-of-the-art approaches and techniques for building recommender systems in order to harness the innate variety of crowd sourced annotations and tags.” (Cathal Gurrin, Informer, July, 2013)Authors and Affiliations
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, Systems and Computing Department, Federal University of Campina Grande, Campina Grande-PB, Brazil
Leandro Balby Marinho
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, Knowledge & Data Engineering Group, University of Kassel, Kassel, Germany
Andreas Hotho, Robert Jäschke, Gerd Stumme
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, Information Systems, University of Hildesheim, Hildesheim, Germany
Alexandros Nanopoulos
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, Social Network Analysis, University of Konstanz, Konstanz, Germany
Steffen Rendle
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, Information Systems, University of Hildesheim, Hildesheim, Germany
Lars Schmidt-Thieme
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, Department of Informatics, Aristotle University, Thessaloniki, Greece
Panagiotis Symeonidis
Bibliographic Information
Book Title: Recommender Systems for Social Tagging Systems
Authors: Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme, Gerd Stumme, … Panagiotis Symeonidis
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-1-4614-1894-8
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2012
Softcover ISBN: 978-1-4614-1893-1
eBook ISBN: 978-1-4614-1894-8
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: IX, 111
Topics: Data Mining and Knowledge Discovery, Information Systems Applications (incl. Internet), Artificial Intelligence