Recommender Systems and the Social Web

Leveraging Tagging Data for Recommender Systems

Authors: Gedikli, Fatih

  • Publication in the field of technical sciences​

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eBook $84.99
price for USA (gross)
  • ISBN 978-3-658-01948-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.00
price for USA
  • ISBN 978-3-658-01947-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

​There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources. The author also proposes algorithms which exploit the user-provided tagging data and produce more accurate recommendations. On the basis of this idea, he shows how tags can be used to explain to the user the automatically generated recommendations in a clear and intuitively understandable form. With his book, Fatih Gedikli gives us an outlook on the next generation of recommendation systems in the Social Web sphere.

About the authors

Dr. Fatih Gedikli is a research assistant in computer science at TU Dortmund, Germany.

Reviews

From the reviews:

“This book presents the results of research conducted in the course of a doctoral study on improving recommendations on the web. … I recommend this book to graduate students and researchers in the field of recommender systems and the social web. It can also serve as inspiration on how to conduct user studies for evaluating various information processing approaches.” (M. Bielikova, Computing Reviews, December, 2013)


Table of contents (7 chapters)

  • Introduction

    Gedikli, Fatih

    Pages 1-6

  • Preliminaries

    Gedikli, Fatih

    Pages 7-32

  • LocalRank – A graph-based tag recommender

    Gedikli, Fatih

    Pages 33-42

  • Improving recommendation accuracy based on item-specific tag preferences

    Gedikli, Fatih

    Pages 43-55

  • Evaluation of explanation interfaces in the form of tag clouds

    Gedikli, Fatih

    Pages 57-68

Buy this book

eBook $84.99
price for USA (gross)
  • ISBN 978-3-658-01948-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.00
price for USA
  • ISBN 978-3-658-01947-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Recommender Systems and the Social Web
Book Subtitle
Leveraging Tagging Data for Recommender Systems
Authors
Copyright
2013
Publisher
Springer Vieweg
Copyright Holder
Springer Fachmedien Wiesbaden
eBook ISBN
978-3-658-01948-8
DOI
10.1007/978-3-658-01948-8
Softcover ISBN
978-3-658-01947-1
Edition Number
1
Number of Pages
XI, 112
Number of Illustrations and Tables
15 b/w illustrations, 14 illustrations in colour
Topics