Recommender Systems and the Social Web
Subtitle: Leveraging Tagging Data for Recommender Systems
By: by Fatih Gedikli
Contributors: SpringerLink (Online service)
Publisher: Springer Fachmedien Wiesbaden; Imprint: Springer Vieweg
Published: 2013
Place of publication: Wiesbaden
Language: eng
Format: [electronic resource] :
Number of pages: 112
ISBN-13: 9783658019488
Subjects: Computer science; Data Mining and Knowledge Discovery; Information organization; Information storage and retrieval systems; Data mining; User Interfaces and Human Computer Interaction; Information retrieval
Description: 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.
Table of contents: title: Recommender Systems; title: Social Tagging; title: Algorithms; title: Explanations. ​.