Introducing Session Relevancy Inspection in Web Page

  • Sutirtha Kumar Guha
  • Anirban Kundu
  • Rana Dattagupta
Part of the Advances in Intelligent Systems and Computing book series (volume 167)

Abstract

In this paper, we propose a new technique for checking the relevancy of sessions created by visitors on a web-page for measuring the web-page ranking, since session of a web-page is considered as an important parameter for web-page ranking calculation in a search engine. It is assumed that session on a web-page depends on the relevancy of the web-page contents with respect to the requirement. A longer session on a web-page may not yield high relevancy of the web-page, hence a threshold value (THV) is considered for individual web-page based on the contents to avoid the probable noise. The threshold value (THV) is calculated by Keyword Matching Index (Kindex) and Data Transfer Speed of the client-server. The Kindex is measured by implementing fuzzy logic on Pattern Matching of requirement and web-page contents. Field Matching information is fetched through hierarchical database.

Keywords

Session Threshold value (THV) Field Matching Pattern Matching Keyword Matching Index (Kindex) 

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References

  1. 1.
  2. 2.
  3. 3.
    Kundu, A., Guha, S.K., Pal, A.R., Sarkar, T., Mandal, S., Duttagupta, R., Mukhopadhyay, D.: Fuzzy Based Multi Agent-System Offering Cost Effective Corporate Environment. The Open Automation and Control System Journal 1, 65–81 (2008)CrossRefGoogle Scholar
  4. 4.
    Do, T.T., Chen, Y., Nguyen, N., Gan, L., Tran, T.D.: A Fast and Efficient Heuristic Nuclear-Norm Algorithm for Affine Rank Minimization. In: ICASSP (2009)Google Scholar
  5. 5.
    Ni, W., Huang, Y., Xie, M.: A Query Dependent Approach to Learning to Rank for Information Retrieval. In: The Ninth International Conference on Web-Age Information Management (2008)Google Scholar
  6. 6.
    Ni, W., Huang, Y.: Online Ranking Algorithm based on Perceptron with Margins. In: The Seventh World Congress on Intelligent Control and Automation, Chongqing, China (2008)Google Scholar
  7. 7.
    Rajapaksha, S.K., Kodagoda, N.: Internal Structure and Semantic Web Link Structure Based Ontology Ranking. In: ICIAF 2008 (2008)Google Scholar
  8. 8.
    Yates, R.B., Davis, E.: Web Page Ranking using Link Attributes. In: The Thirteenth International World Wide Web Conference, NewYork, USA (2008)Google Scholar
  9. 9.
    He, C., Wang, C., Zhong, Y.-X., Li, R.-F.: A Survey on Learning to Rank. In: Seventh International Conference on Machine Learning and Cybernetics, Kunming (2008)Google Scholar
  10. 10.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American Magazine (May 17, 2001), http://www.sciam.com/article.cfm?id=the-semantic-web&print=true (retrieved March 26, 2008)

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Sutirtha Kumar Guha
    • 1
    • 3
  • Anirban Kundu
    • 2
    • 3
  • Rana Dattagupta
    • 4
  1. 1.Seacom Engineering CollegeHowrahIndia
  2. 2.Kuang-Chi Institute of Advanced TechnologyShenzhenP.R. China
  3. 3.Innovation Research LabJalpaiguriIndia
  4. 4.Jadavpur UniversityKolkataIndia

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