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

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)

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.

Contents

– 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.