rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Recommender Systems and the Social Web
    sub title: Leveraging Tagging Data for Recommender Systems
  name:
    name part: Gedikli, Fatih.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 112 p. 29 illus., 14 illus. in color. online resource.
  abstract: 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: Recommender Systems -- Social Tagging -- Algorithms -- Explanations.
  note: by Fatih Gedikli.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Information storage and retrieval
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Information Storage and Retrieval
  subject:
    topic: User Interfaces and Human Computer Interaction
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783658019488
  identifier: http://dx.doi.org/10.1007/978-3-658-01948-8
  record info:
    record creation date: 130330
    record change date: 20151125211120.0
    record identifier: 978-3-658-01948-8