• DocumentCode
    950762
  • Title

    Using document access sequences to recommend customized information

  • Author

    Bauer, Travis ; Leake, David

  • Author_Institution
    Indiana Univ., IN, USA
  • Volume
    17
  • Issue
    6
  • fYear
    2002
  • Firstpage
    27
  • Lastpage
    33
  • Abstract
    WordSieve, a text analysis algorithm, uses a competitive-network-learning approach to learn topic-relevant keywords in real time with no predetermined corpus. You can use these keywords to form search engine queries to suggest relevant documents to the user.
  • Keywords
    search engines; text analysis; unsupervised learning; WordSieve; competitive network-learning; customization agents; information customization; search engine queries; text analysis; topic-relevant keywords; Context modeling; Feedback; Frequency; Indexing; Information analysis; Information retrieval; Search engines; Testing; Text analysis; Web sites;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2002.1134359
  • Filename
    1134359
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=950762