• DocumentCode
    3540031
  • Title

    Solving hanging relevancy using genetic algorithm

  • Author

    Singh, Ashutosh Kumar ; Ravi, Kumar P. ; Leng, Alex Goh Kwang

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Curtin Univ., Miri, Malaysia
  • fYear
    2012
  • fDate
    14-15 Aug. 2012
  • Firstpage
    9
  • Lastpage
    12
  • Abstract
    Continuous growth of hanging pages with Web makes a significant problem for ranking in the information retrieval. Exclusion of these pages in ranking calculation can give biased/inconsistent result. On the other hand inclusion of these pages will reduce the speed significantly. However most of the IR ranking algorithms exclude the hanging pages. But there are relevant and important hanging pages on the Web and they cannot be ignored because of the complexity in computation and time. In our proposed method, we include the relevant hanging pages in the ranking. Relevancy or non-relevancy of hanging pages is achieved by application of Genetic Algorithm (GA).
  • Keywords
    Web sites; genetic algorithms; information retrieval; IR ranking algorithm; Web service; genetic algorithm; hanging page nonrelevancy; hanging page relevancy; information retrieval; page ranking; relevant hanging page; Educational institutions; Genetic algorithms; Optimization; Sociology; Statistics; Web pages; Genetic Algorithm; Hanging Pages; Hanging Relevancy; PageRank;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on
  • Conference_Location
    Jalarta
  • Print_ISBN
    978-1-4673-1459-6
  • Type

    conf

  • DOI
    10.1109/URKE.2012.6319593
  • Filename
    6319593