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Recommender Systems and the Social Web [electronic resource] : Leveraging Tagging Data for Recommender Systems / by Fatih Gedikli.

By: Gedikli, Fatih [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2013Description: XI, 112 p. 29 illus., 14 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783658019488.Subject(s): Computer science | Data mining | Information storage and retrieval | User interfaces (Computer systems) | Computer Science | Data Mining and Knowledge Discovery | Information Storage and Retrieval | User Interfaces and Human Computer InteractionAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
Recommender Systems -- Social Tagging -- Algorithms -- Explanations.
In: Springer eBooksSummary: 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.
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Recommender Systems -- Social Tagging -- Algorithms -- Explanations.

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.

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