Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks

  • Hyoung-rae Kim
  • Philip K. Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4198)


Personalized web search incorporates an individual user’s interests when deciding relevant results to return. While, most web search engines are usually designed to serve all users, without considering the interests of individual users. We propose a method to (re)rank the results from a search engine using a learned user profile, called a user interest hierarchy (UIH), from web pages that are of interest to the user. The user’s interest in web pages will be determined implicitly, without directly asking the user. Experimental results indicate that our personalized ranking methods, when used with a popular search engine, can yield more potentially interesting web pages for individual users.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, C.R.: A Machine Learning Approach to Web Personalization. Ph.D. thesis. University of Washington, Department of Computer Science and Engineering (2002)Google Scholar
  2. 2.
    Ben Schafer, J., Konstan, J.A., Riedl, J.: Electronic commerce recommender applications. Journal of Data Mining and Knowledge Discovery 5, 115–152 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bharat, K., Mihaila, G.A.: When experts agree: using non-affiliated experts to rank popular topics. In: Proc. of the 10th Intl. World Wide Web Conference (2001)Google Scholar
  4. 4.
    Brin, S., Motwani, R., Page, L., Winograd, T.: What can you do with a web in your pocket. In: Bulletin of the IEEE Computer Society Technical Committee on Data Engineering (1998)Google Scholar
  5. 5.
    Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Visualization of navigation pat-terns on web site using model based clustering. Technical Report MSR-TR-00-18, Microsoft Research, Microsoft Corporation, Redmond, WA (2000)Google Scholar
  6. 6.
    Chan, P.K.: A non-invasive learning approach to building web user profiles. In: KDD 1999 Workshop on Web Usage Analysis and User Profiling, 7–12 (1999)Google Scholar
  7. 7.
    Chen, L., Sycara, K.: WebMate: A personal agent for browsing and searching. In: Proc. of the 2nd Intl. conf. on Autonomous Agents, pp. 132–139 (1998)Google Scholar
  8. 8.
    Croft, W.B., Das, R.: Experiments with query acquisition and use in document retrieval systems. In: Proc. of 13th ACM SIGIR (1989)Google Scholar
  9. 9.
    Croft, W.B., Thompson, R.T.: I3R: A new approach to the design of document retrieval systems. Journal of the Americal Society for Information Science 38, 389–404 (1987)CrossRefGoogle Scholar
  10. 10.
    Delaney, K.J.: Study questions whether google really is better. Wall Street Journal, May 25 (2004) B.1
  11. 11.
    Eirinaki, M., Lampos, C., Paulakis, S., Vazirgiannis, M.: Web personalization integrating content semantics and navigational patterns. In: Workshop on Web Information and Data Management, pp. 72–79 (2004)Google Scholar
  12. 12.
    Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Englewood Cliffs (1992)Google Scholar
  13. 13.
    Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: Proc. 2000 Conference on Intelligent User Interfaces (2000)Google Scholar
  14. 14.
    Google co. (2004),
  15. 15.
    Grossman, D., Frieder, O., Holmes, D., Roberts, D.: Integrating structured data and text: A relational approach. Journal of the American Society for Information Science 48(2) (1997)Google Scholar
  16. 16.
    Harper, D.J.: Relevance Feedback in Document Retrieval Systems: An Evaluation of Probabilistic Strategies. Ph.D. Thesis, Computer Laboratory, University of Cambridge (1980)Google Scholar
  17. 17.
    Haveliwala, T.H.: Efficient computation of PageRank. Technical Report, Stanford University Database Group (1999),
  18. 18.
    Haveliwala, T.H.: Topic-sensitive PageRank. In: Proc. of the 11th Intl. World Wide Web Conference, Honolulu, Hawaii (2002)Google Scholar
  19. 19.
    Jeh, G., Widom, J.: Scaling personalized web search. In: Proc. of the 12th Intl. Conference on World Wide Web, Budapest, Hungary, pp. 20–24 (2003)Google Scholar
  20. 20.
    Kim, D., Atluri, V., Bieber, M., Adam, N., Yesha, Y.: A clickstream-based collaborative filtering personalization model: towards a better performance. In: Workshop on Web Information and Data Management (2004)Google Scholar
  21. 21.
    Kim, H., Chan, P.K.: Implicit indicator for interesting web pages. In: International Conference on Web Information Systems and Technologies, pp. 270–277 (2005)Google Scholar
  22. 22.
    Kim, H., Chan, P.K.: Identifying variable-length meaningful phrases with correlation functions. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 30–38. IEEE press, Los Alamitos (2004)CrossRefGoogle Scholar
  23. 23.
    Kim, H., Chan, P.K.: Learning implicit user interest hierarchy for context in personalization. In: International Conference on Intelligent User Interfaces, pp. 101–108 (2003)Google Scholar
  24. 24.
    Li, W.S., Vu, Q., Agrawal, D., Hara, Y., Takano, H.: PowerBookmarks: A System for personalizable web information organization, sharing, and management. In: Proc. of the 8th Intl. World Wide Web Conference, Toronto, Canada (1999)Google Scholar
  25. 25.
    Liu, F., Yu, C., Meng, W.: Personalized web search by mapping user queries to categories. In: CIKM 2002. ACM Press, Virginia (2002)Google Scholar
  26. 26.
    Maarek, Y.S., Ben-Shaul, I.Z.: Automatically organizing bookmarks per contents. In: Proc. 5th International World Wide Web Conference (1996)Google Scholar
  27. 27.
    Mitchell, T.M.: Machine Learning. McGraw Hill, New York (1997)zbMATHGoogle Scholar
  28. 28.
    Mobasher, B., Cooley, R., Srivastava, J.: Creating adaptive web sites through usage-based clustering of URLs. In: Proc. 1999 IEEE Knowledge and Data Engineering Exchange Workshop, pp. 19–25 (1999)Google Scholar
  29. 29.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford University Database Group (1998),
  30. 30.
    Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting web sites. Machine Learning 27(3), 313–331 (1997)CrossRefGoogle Scholar
  31. 31.
    Salton, G., Waldstein, R.G.: Term relevance weights in online information retrieval. Information Processing and Management 14, 29–35 (1978)CrossRefGoogle Scholar
  32. 32.
    Shahabi, C., Banaei-Kashani, F.: Efficient and anonymous web-usage mining for web personalization. INFORMS Journal on Computing-Special Issue on Data Mining 15(2) (2003)Google Scholar
  33. 33.
    Stefani, A., Strapparava, C.: Exploiting nlp techniques to build user model for web sites: The use of worldnet in SiteIF project. In: Proc. 2nd Workshop on Adaptive Systems and User Modeling on the WWW (1999)Google Scholar
  34. 34.
    Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: Proc. SIGIR (2005)Google Scholar
  35. 35.
    van Rijsbergen, C.J.: Information Retrieval, pp. 68–176. Butterworths, London (1979)Google Scholar
  36. 36.
    Vivisimo co. (2004),
  37. 37.
    Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval. Information Processing & Management 22(6), 465–476 (1986)CrossRefGoogle Scholar
  38. 38.
    Wexelblat, A., Maes, P.: Footprints: History-rich web browsing. In: Proc. Conference on Computer-Assisted Information Retrieval (RIAO), pp. 75–84 (1997)Google Scholar
  39. 39.
    Yan, T.W., Jacobsen, M., Garcia-Molina, H., Dayal, U.: From user access patterns to dynamic hypertext linking. In: Proc. 5th International World Wide Web Conference (1996)Google Scholar
  40. 40.
    Zukerman, I., Albrecht, D.W., Nicholson, A.E.: Predicting users’ requests on the WWW. In: Proc. of the 7th Intel. Conference on User Modeling (UM), Banff, Canada, pp. 275–284 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyoung-rae Kim
    • 1
  • Philip K. Chan
    • 2
  1. 1.Web Intelligence LaboratoryGangneung-shi, Gangwon-doSouth Korea
  2. 2.Department of Computer SciencesFlorida Institute of TechnologyMelbourneUSA

Personalised recommendations