Recommender Systems and the Social Web: Leveraging Tagging Data for Recommender Systems

Author:   Fatih Gedikli
Publisher:   Springer Fachmedien Wiesbaden
Edition:   2013 ed.
ISBN:  

9783658019471


Pages:   112
Publication Date:   10 April 2013
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Recommender Systems and the Social Web: Leveraging Tagging Data for Recommender Systems


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Overview

​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.

Full Product Details

Author:   Fatih Gedikli
Publisher:   Springer Fachmedien Wiesbaden
Imprint:   Springer Vieweg
Edition:   2013 ed.
Dimensions:   Width: 14.80cm , Height: 1.00cm , Length: 21.00cm
Weight:   1.707kg
ISBN:  

9783658019471


ISBN 10:   3658019476
Pages:   112
Publication Date:   10 April 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Recommender Systems.- Social Tagging.- Algorithms.- Explanations. ​

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)


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)


Author Information

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

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