• DocumentCode
    2447720
  • Title

    Research on Personalized Topic Distillation Algorithm

  • Author

    Kai-zhong Jiang ; Zhao Lu ; Yu Yan ; Jun-zhong Gu

  • Author_Institution
    Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai
  • fYear
    2008
  • fDate
    July 27 2008-Aug. 1 2008
  • Firstpage
    244
  • Lastpage
    249
  • Abstract
    This paper focuses on personalized information retrieval which is a research focus in Web information service, and itpsilas the key technology of enhance retrieval quality. As a user concept map composed of some concepts that extended from user queries based on user interests description, it´s the embodiment of user´s true intention. Each concept weight in the concept map is defined as a probability, and the whole weighted concept map can reflect the intensity distribution of user interests in the field. Thus semantic similarity and algorithm of two concepts can be defined with concept weight. In this paper, we proposes an improving approach of HITS algorithm with the topological structure of concept map. Experiment shows the algorithm is effective and feasible, and it can improve the quality of Web search engines efficiently.
  • Keywords
    Internet; information filtering; information services; probability; search engines; HITS algorithm; Web information service; Web search engine; intensity distribution; personalized information retrieval; personalized topic distillation algorithm; probability; retrieval quality; semantic similarity; topological structure; user concept map; user intention; user interest; user queries; Computer science; Education; Educational institutions; Information retrieval; Information technology; OWL; Ontologies; Search engines; Web pages; Web search;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing in the Global Information Technology, 2008. ICCGI '08. The Third International Multi-Conference on
  • Conference_Location
    Athens
  • Print_ISBN
    978-0-7695-3275-2
  • Electronic_ISBN
    978-0-7695-3275-2
  • Type

    conf

  • DOI
    10.1109/ICCGI.2008.8
  • Filename
    4591375