Using Hyperlink Features to Personalize Web Search

  • Mehmet S. Aktas
  • Mehmet A. Nacar
  • Filippo Menczer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3932)


Personalized search has gained great popularity to improve search effectiveness in recent years. The objective of personalized search is to provide users with information tailored to their individual contexts. We propose to personalize Web search based on features extracted from hyperlinks, such as anchor terms or URL tokens. Our methodology personalizes PageRank vectors by weighting links based on the match between hyperlinks and user profiles. In particular, here we describe a profile representation using Internet domain features extracted from URLs. Users specify interest profiles as binary vectors where each feature corresponds to a set of one or more DNS tree nodes. Given a profile vector, a weighted PageRank is computed assigning a weight to each URL based on the match between the URL and the profile. We present promising results from an experiment in which users were allowed to select among nine URL features combining the top two levels of the DNS tree, leading to 29 pre-computed PageRank vectors from a Yahoo crawl. Personalized PageRank performed favorably compared to pure similarity based ranking and traditional PageRank.


Query Time Anchor Text International World Wide Ranking Mechanism IEEE Intelligent System 
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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  1. 1.
    van Rijsbergen, C.: Information Retrieval, 2nd edn. Butterworths, London (1979)Google Scholar
  2. 2.
    Salton, G., McGill, M.: An Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)zbMATHGoogle Scholar
  3. 3.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks 30, 107–117 (1998)Google Scholar
  4. 4.
    Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46, 604–632 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the Web. Technical report, Stanford University Database Group (1998)Google Scholar
  6. 6.
    Brin, S., Motwani, R., Page, L., Winograd, T.: What can you do with a Web in your pocket. IEEE Data Engineering Bulletin 21, 37–47 (1998)Google Scholar
  7. 7.
    Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A., Raghavan, S.: Searching the web. ACM Trans. Inter. Tech. 1, 2–43 (2001)CrossRefGoogle Scholar
  8. 8.
    Langville, A.N., Meyer, C.D.: Deeper inside PageRank. Internet Mathematics (forthcoming)Google Scholar
  9. 9.
    Langville, A.N., Meyer, C.D.: A survey of eigenvector methods of Web information retrieval. SIAM Review (forthcoming)Google Scholar
  10. 10.
    Haveliwala, T.: Topic-sensitive PageRank. In: Lassner, D., De Roure, D., Iyengar, A. (eds.) Proc. 11th International World Wide Web Conference. ACM Press, New York (2002)Google Scholar
  11. 11.
    Richardson, M., Domingos, P.: The intelligent surfer: Probabilistic combination of link and content information in PageRank. In: Advances in Neural Information Processing Systems, vol. 14, pp. 1441–1448. MIT Press, Cambridge, MA (2002)Google Scholar
  12. 12.
    Jeh, G., Widom, J.: Scaling personalized Web search. In: Proc. 12th International World Wide Web Conference (2003)Google Scholar
  13. 13.
    Haveliwala, T.: Efficient computation of pagerank. Technical report, Stanford Database Group (1999)Google Scholar
  14. 14.
    Kamvar, S.D., Haveliwala, T.H., Manning, C.D., Golub, G.H.: Exploiting the block structure of the Web for computing PageRank. Technical report, Stanford University (2003)Google Scholar
  15. 15.
    Kamvar, S.D., Haveliwala, T.H., Manning, C.D., Golub, G.H.: Extrapolation methods for accelerating the computation of pagerank. In: Proc. 12th International World Wide Web Conference (2003)Google Scholar
  16. 16.
    Kamvar, S.D., Haveliwala, T.H., Golub, G.H.: Adaptive methods for the computation of PageRank. Technical report, Stanford University (2003)Google Scholar
  17. 17.
    Eiron, N., McCurley, K., Tomlin, J.: Ranking the Web frontier. In: Proc. 13th conference on World Wide Web, pp. 309–318. ACM Press, New York (2004)CrossRefGoogle Scholar
  18. 18.
    Acharyya, S., Ghosh, J.: Outlink estimation for pagerank computation under missing data. In: Alt. Track Papers and Posters Proc. 13th International World Wide Web Conference, pp. 486–487 (2004)Google Scholar
  19. 19.
    Pitkow, J., Schutze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., Breuel, T.: Personalized Search. Communication of ACM 42(9) (2002)Google Scholar
  20. 20.
    Eirinaki, M., Vazirgiannis, M.: Web Mining for Web Personalization. ACM Transactions on Internet Technologies (ACM TOIT) 3(1)Google Scholar
  21. 21.
    Mostafa, J.: Information Customization. IEEE Intelligent Systems 17.6 (2002)Google Scholar
  22. 22.
    Ha, S.H.: Helping Online Customers Decide through Web Personalization. IEEE Intelligent Systems 17.6 (2002)Google Scholar
  23. 23.
    Jenamani, M., Mohapatra, P., Ghose, S.: Online Customized Index Synthesis in Commercial Web Sites. IEEE Intelligent Systems 17.6 (2002)Google Scholar
  24. 24.
    Nasraoui, O., Petenes, C.: Combining Web Usage Mining and Fuzzy Inference for Website Personalization. In: Proc. of WebKDD 2003 - KDD Workshop on Web mining as a Premise to Effective and Intelligent Web Applications, Washington DC, August 2003, p. 37 (2003)Google Scholar
  25. 25.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalizaton based on association rule discovery from Web usage data. In: ACM Workshop on Web information and data management, Atlanta, GAGoogle Scholar
  26. 26.
    Li, J., Zaiane, O.: Using Distinctive Information Channels for a Mission-based Web-Recommender System. In: Proc. of WebKDD-2004 workshop on Web Mining and Web Usage Analysis, part of the ACM KDD: Knowledge Discovery and Data Mining Conference, Seattle, WA (2004)Google Scholar
  27. 27.
    Davison, B.D.: Topical locality in the Web. In: Proceedings of the 1st International World Wide Web Conference, Geneva (1994),
  28. 28.
    Bradshaw, S., Hammond, K.: Automatically Indexing Research Papers Using Text Surrounding Citations. In: Working Notes of the Workshop on Intelligent Information Systems, Sixteenth National Conference on Artificial Intelligence, Orlando, FL, July 18-19Google Scholar
  29. 29.
    Liu, F., Yu, C., Meng, W.: Personalized Web Search For Improving Retrieval Effectiveness. IEEE Transactions on Knowledge and Data Engineering (January 2004)Google Scholar
  30. 30.
    BaezaYates, R., Davis, E.: Web Page Ranking using Link Attributes. In: WWW 2004, May 17-22, New York, USA (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mehmet S. Aktas
    • 1
  • Mehmet A. Nacar
    • 1
  • Filippo Menczer
    • 1
    • 2
  1. 1.Computer Science DepartmentUSA
  2. 2.School of InformaticsIndiana UniversityBloomingtonUSA

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