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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jansen, B.J., Spink, A., Saracevic, T.: Real life, Real users, and Real Needs: A Study and Analysis of User Queries on the Web. Information Processing and Management 36(2), 207–227 (2000)CrossRefGoogle Scholar
  2. 2.
    Miller, G.A.: The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information. The Psychological Review 63, 81–97 (1956)CrossRefGoogle Scholar
  3. 3.
    Biittcher, S., Clark, C.L.A., Cormank, G.V.: Information retrieval – Implementing and Evaluating Search Engines, p. 606. The MIT Press, Cambridge (2010)Google Scholar
  4. 4.
    Candam, K.S., Sapino, H.L.: Data Management for Multimedia Retrieval, p. 489. Cambridge Univ. Press, Cambridge (2010)Google Scholar
  5. 5.
    Fang, H., Tao, T., Zhai, C.: A Formal Study of Information Retrieval Heuristics. In: Proc.of 27th Int’l Conf. ACM SIGIR, pp. 49–56 (2004)Google Scholar
  6. 6.
    Krovetz, R., Croft, W.B.: Lexical Ambiguity and Information Retrieval. ACM Trans. on Information Systems (TOIS) 10(2), 115–141 (1992)CrossRefGoogle Scholar
  7. 7.
    Rocchio, J.J.: Relevance Feedback in Information Retrieval. The Smart Retrieval System- Experiments in Automatic Document Processing, 313–323 (1971)Google Scholar
  8. 8.
    Onoda, T., Murata, H., Yamada, S.: SVM-based Interactive Document Retrieval with Active Learning. New Generation Computing 26(1), 49–61 (2008)zbMATHCrossRefGoogle Scholar
  9. 9.
    Onoda, T., Murata, H., Yamada, S.: One Class Classification Methods Based on Non-Relevance Feedback Document Retrieval. In: Proc.of 2006 IEEE/WIC/ACM Int’l Conf.on Web Intelligence and Intelligent Agent Technology, pp. 393–396 (2006)Google Scholar
  10. 10.
    Yamamoto, T., Nakamura, S., Tanaka, K.: Rerank-by-example: Efficient browsing of web search results. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 801–810. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Karube, T., Shizuki, B., Tanaka, J.: A Ranking Interface Based on Interactive Evaluation of Search Results. In: Proc. of WISS 2007 (2007) (in Japanese)Google Scholar
  12. 12.
    Jeh, G., Widom, J.: Scaling Personalized Web Search. In: Proc.of 12th World Wide Web Conference (WWW), pp. 271–279 (2003) Google Scholar
  13. 13.
    Matsuo, Y., Ishizuka, M.: Keyword Extraction from a Document Using Word Co-occurrence Statiscal Information. Journal of Japanese Society for Artificial Intelligence 17(3), 213–227 (2002) (in Japanese)Google Scholar
  14. 14.
  15. 15.
    Robertson, S.E., Walker, S., Jones, S., H-Beaulieu, M., Gatford, M.: Okapi at TREC-3. In: Proc.of 3rd Text Retrieval Conference, pp. 109–126 (1994)Google Scholar

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

Personalised recommendations