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Web-Pages Re-ranking, Based on Relevant/Irrelevant Feedback Information

  • Toyohide Watanabe
  • Kenji Matsuoka
Part of the Studies in Computational Intelligence book series (SCI, volume 376)

Abstract

A keyword-based retrieval engine, which is most usable recently, extracts appropriate Web-pages by means of keywords in user-specified queries. However, it is not always easy to extract the user-preferred Web-pages correctly, because the user-specified keywords have several meanings in many cases. In such case, we must find out relevant Web-pages and exclude irrelevant Web-pages. Also, in case that we cannot retrieve the desirable Web-pages, we must retry after modifying the original query. In this paper, we propose an advanced Web-page retrieval method to find out user-preferred Web-pages in case that relevant pages could not be extracted. The idea is to make use of user’s unconscious reactions to judge which pages are relevant or not, when the retrieved results were listed up. Our method is to infer user-preference on the basis of relevant or irrelevant indications for the page and reflect the inferred preference into the next retrieval query with a view to improving the retrieved results.

Keywords

Average Precision Feedback Information Index Word Relevant Page Target Page 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Toyohide Watanabe
    • 1
  • Kenji Matsuoka
    • 1
  1. 1.Department of Systems and Social Informatics, Graduate School of Information ScienceNagoya UniversityNagoyaJapan

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