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:
    non sort: A
    title: Generative Theory of Relevance
  name:
    name part: Lavrenko, Victor.
    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: 2009
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XX, 197 p. 31 illus. online resource.
  abstract: A modern information retrieval system must have the capability to find, organize and present very different manifestations of information – such as text, pictures, videos or database records – any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way. Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events. Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.
  table of contents: Relevance -- A Generative View of Relevance -- Generative Density Allocation -- Retrieval Scenarios -- Conclusion.
  note: by Victor Lavrenko.
  subject:
    topic: Computer science
  subject:
    topic: Algorithms
  subject:
    topic: Mathematical statistics
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Computer Science
  subject:
    topic: Information Storage and Retrieval
  subject:
    topic: Probability and Statistics in Computer Science
  subject:
    topic: Algorithm Analysis and Problem Complexity
  classification: QA75.5-76.95
  classification: 025.04
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: The Information Retrieval Series, 26
  identifier: 9783540893646
  identifier: http://dx.doi.org/10.1007/978-3-540-89364-6
  record info:
    record creation date: 100301
    record change date: 20151030031320.0
    record identifier: 978-3-540-89364-6