A Website Mining Model Centered on User Queries

  • Ricardo Baeza-Yates
  • Barbara Poblete
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4289)


We present a model for mining user queries found within the access logs of a website and for relating this information to the website’s overall usage, structure and content. The aim of this model is to discover, in a simple way, valuable information to improve the quality of the website, allowing the website to become more intuitive and adequate for the needs of its users. This model presents a methodology of analysis and classification of the different types of queries registered in the usage logs of a website, such as queries submitted by users to the site’s internal search engine and queries on global search engines that lead to documents in the website. These queries provide useful information about topics that interest users visiting the website and the navigation patterns associated to these queries indicate whether or not the documents in the site satisfied the user’s needs at that moment.


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  1. 1.
    Berendt, B., Spiliopoulou, M.: Analysis of navigation behaviour in web sites integrating multiple information systems. VLDB Journal (special issue on “Databases and the Web”)  9(1), 56–75 (2000)CrossRefGoogle Scholar
  2. 2.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)CrossRefGoogle Scholar
  3. 3.
    Cooley, R., Tan, P.N., Srivastava, J.: Discovery of interesting usage patterns from web data. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS, vol. 1836, pp. 163–182. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Baeza-Yates, R.: Web usage mining in search engines. In: Scime, A. (ed.) Web Mining: Applications and Techniques, pp. 307–321. Idea Group (2004)Google Scholar
  5. 5.
    Perkowitz, M., Etzioni, O.: Adaptive web sites: an AI challenge. IJCAI (1), 16–23 (1997)Google Scholar
  6. 6.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43(8), 142–151 (2000)CrossRefGoogle Scholar
  7. 7.
    Spiliopoulou, M.: Web usage mining for web site evaluation. Commun. ACM 43(8), 127–134 (2000)CrossRefGoogle Scholar
  8. 8.
    Batista, P., Silva, M.J.: Mining on-line newspaper web access logs. In: Ricci, F., Smyth, B. (eds.) Proceedings of the AH 2002, Workshop on Recommendation and Personalization in eCommerce, pp. 100–108 (2002)Google Scholar
  9. 9.
    Cooley, R., Tan, P., Srivastava, J.: Websift: the web site information filter system. In: KDD Workshop on Web Mining, San Diego, CA. Springer, Heidelberg (1999) (in press)Google Scholar
  10. 10.
    Masseglia, F., Poncelet, P., Teisseire, M.: Using data mining techniques on web access logs to dynamically improve hypertext structure. ACM SigWeb Letters 8(3), 1–19 (1999)Google Scholar
  11. 11.
    Huang, Z., Ng, J., Cheung, D., Ng, M., Ching, W.: A cube model for web access sessions and cluster analysis. In: Kohavi, R., Masand, B., Spiliopoulou, M., Srivastava, J. (eds.) WebKDD 2001. LNCS (LNAI), vol. 2356, pp. 47–57. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Nasraoui, O., Krishnapuram, R.: An evolutionary approach to mining robust multi-resolution web profiles and context sensitive url associations. Intl’ Journal of Computational Intelligence and Applications 2(3), 339–348 (2002)CrossRefGoogle Scholar
  13. 13.
    Nasraoui, O., Petenes, C.: Combining web usage mining and fuzzy inference for website personalization. In: Proceedings of the WebKDD workshop, pp. 37–46 (2003)Google Scholar
  14. 14.
    Pei, J., Han, J., Mortazavi-asl, B., Zhu, H.: Mining access patterns efficiently from web logs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 396–407 (2000)Google Scholar
  15. 15.
    Perkowitz, M., Etzioni, O.: Adaptive web sites: automatically synthesizing web pages. In: AAAI 1998/IAAI 1998: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, pp. 727–732. American Association for Artificial Intelligence, Menlo Park (1998)Google Scholar
  16. 16.
    Xue, G.R., Zeng, H.J., Chen, Z., Ma, W.Y., Lu, C.J.: Log mining to improve the performance of site search. In: WISEW 2002: Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw 2002), p. 238. IEEE Computer Society, Washington (2002)Google Scholar
  17. 17.
    Baeza-Yates, R.A., Hurtado, C.A., Mendoza, M.: Query clustering for boosting web page ranking. In: Favela, J., Menasalvas, E., Chávez, E. (eds.) AWIC 2004. LNCS (LNAI), vol. 3034, pp. 164–175. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Baeza-Yates, R.A., Hurtado, C.A., Mendoza, M.: Query recommendation using query logs in search engines. In: Lindner, W., Mesiti, M., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 588–596. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Kang, I.H., Kim, G.: Query type classification for web document retrieval. In: SIGIR 2003: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 64–71. ACM Press, New York (2003)CrossRefGoogle Scholar
  20. 20.
    Sieg, A., Mobasher, B., Lytinen, S., Burke, R.: Using concept hierarchies to enhance user queries in web-based information retrieval. In: IASTED International Conference on Artificial Intelligence and Applications (2004)Google Scholar
  21. 21.
    Radlinski, F., Joachims, T.: Query chains: learning to rank from implicit feedback. In: KDD 2005: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 239–248. ACM Press, New York (2005)CrossRefGoogle Scholar
  22. 22.
    Davison, B.D., Deschenes, D.G., Lewanda, D.B.: Finding relevant website queries. In: Poster Proceedings of the Twelfth International World Wide Web Conference, Budapest, Hungary (2003)Google Scholar
  23. 23.
    Baeza-Yates, R.: Mining the web (in spanish). El profesional de la información (The Information Professional) 13(1), 4–10 (2004)Google Scholar
  24. 24.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems 1(1), 5–32 (1999)Google Scholar
  25. 25.
    Mobasher, B.: Web usage mining and personalization. In: Singh, M.P. (ed.) Practical Handbook of Internet Computing. Chapman Hall & CRC Press, Baton Rouge (2004)Google Scholar
  26. 26.
    Poblete, B.: A web mining model and tool centered in queries. in Computer Science, CS Dept., Univ. of Chile (2004)Google Scholar
  27. 27.
    Pirolli, P.: Computational models of information scent-following in a very large browsable text collection. In: CHI 1997: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 3–10. ACM Press, New York (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ricardo Baeza-Yates
    • 1
    • 2
    • 3
  • Barbara Poblete
    • 1
    • 2
  1. 1.Web Research Group, Technology DepartmentUniversity Pompeu FabraBarcelonaSpain
  2. 2.Center for Web Research, CS DepartmentUniversity of ChileSantiagoChile
  3. 3.Yahoo! ResearchBarcelonaSpain

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