Recommender Systems and the Social Web

Leveraging Tagging Data for Recommender Systems

  • Fatih Gedikli

Table of contents

  1. Front Matter
    Pages i-xi
  2. Fatih Gedikli
    Pages 1-6
  3. Fatih Gedikli
    Pages 7-32
  4. Fatih Gedikli
    Pages 89-92
  5. Back Matter
    Pages 93-112

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.


-  Recommender Systems

-  Social Tagging

-  Algorithms

-  Explanations


Target Groups

·         Researchers and students of computer science

·         Computer and web programmers


The Author

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

Authors and affiliations

  • Fatih Gedikli
    • 1
  1. 1., Fakultät für InformatikTU DortmundDortmundGermany

Bibliographic information

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