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: Context-Aware Collaborative Prediction
  name:
    name part: Wu, Shu.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Liu, Qiang.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Wang, Liang.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Tan, Tieniu.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2017
    edition: 1st ed. 2017.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 69 p. 9 illus., 8 illus. in color. online resource.
  abstract: This book presents two collaborative prediction approaches based on contextual representation and hierarchical representation, and their applications including context-aware recommendation, latent collaborative retrieval and click-through rate prediction. The proposed techniques offer significant improvements over current methods, the key determinants being the incorporated contextual representation and hierarchical representation. To provide a background to the core ideas presented, it offers an overview of contextual modeling and the theory of contextual representation and hierarchical representation, which are constructed for the joint interaction of entities and contextual information. The book offers a rich blend of theory and practice, making it a valuable resource for students, researchers and practitioners who need to construct systems of information retrieval, data mining and recommendation systems with contextual information.
  table of contents: 1. Introduction -- 2. Context-Aware Collaborative Prediction -- 3. Context Operation -- 4. Hierarchical Representation -- 5. Context-Aware Recurrent Structure -- 6. Performance of Different Collaborative Prediction Tasks.
  note: by Shu Wu, Qiang Liu, Liang Wang, Tieniu Tan.
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Application software
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Information Storage and Retrieval
  subject:
    topic: Information Systems Applications (incl. Internet)
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Artificial Intelligence
  classification: QA75.5-76.95
  classification: 025.04
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computer Science
  identifier: 9789811053733
  identifier: https://doi.org/10.1007/978-981-10-5373-3
  record info:
    record creation date: 180310
    record change date: 20191023182513.0
    record identifier: 978-981-10-5373-3