World Wide Web

, Volume 8, Issue 3, pp 317–345 | Cite as

Mining User Preferences, Page Content and Usage to Personalize Website Navigation

  • Sergio Flesca
  • Sergio Greco
  • Andrea Tagarelli
  • Ester Zumpano
Article

Abstract

The growing availability of information on the Web has raised a challenging problem: can a Web-based information system tailor itself to different user requirements with the ultimate goal of personalizing and improving the users' experience in accessing the contents of a website? This paper proposes a new approach to website personalization based on the exploitation of user browsing interests together with content and usage similarities among Web pages. The outcome is the delivery of page recommendations which are strictly related to the navigational purposes of visitors and their actual location within the cyberspace of the website. Our approach has been used effectively for developing a non-invasive system which allows Web users to navigate through potentially interesting pages without having a basic knowledge of the website structure.

Keywords

information processing on the Web databases and information retrieval personalization Web mining 

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References

  1. [1]
    M. Balabanovic and Y. Shoham, “Content-based, collaborative recommendation,” Communications of the ACM 40(3), 1997, 66–72.Google Scholar
  2. [2]
    S. Chakrabarti, Mining the Web—Discovering Knowledge from Hypertext Data, Morgan Kaufmann Publishers, Elsevier Science, 2002.Google Scholar
  3. [3]
    P. K. Chan, “A non-invasive learning approach to building web user profiles,” in Workshop on Web Usage Analysis and User Profiling (WebKDD'99), 1999, pp. 39–55.Google Scholar
  4. [4]
    F. Coenen, G. Swinnen, K. Vanhoof and G. Wets, “A framework for self adaptive websites: Tactical versus strategic changes,” in Workshop on Web Mining for E-commerce—Challenges and Opportunities (WebKDD00), 2000.Google Scholar
  5. [5]
    R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, ACM Press Books, 1999.Google Scholar
  6. [6]
    M. Eirinaki and M. Vazirgiannis, “Web mining for web personalization,” ACM Transactions on Internet Technology 3(1), 2003, 1–27.Google Scholar
  7. [7]
    J. Fink, A. Kobsa and A. Nill, “Adaptable and adaptive information provision for all users, including disabled and elderly people,” The New Review of Hypermedia and Multimedia 4, 1998, 163–188.Google Scholar
  8. [8]
    Y. Fu, K. Sandhu and M.-Y. Shih, “A generalization-based approach to clustering of web usage sessions,” in Workshop on Web Usage Analysis and User Profiling (WebKDD'99), 1999, pp. 21–38.Google Scholar
  9. [9]
    D. Goldberg, D. Nichols, B. M. Oki and D. B. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM 35(12), 1992, 61–70.Google Scholar
  10. [10]
    N. Good, B. Schafer, J. Konstan, A. Borchers, B. Sarwar, J. Herlocker and J. Riedl, “Combining collaborative filtering with personal agents for better recommendations,” in Proc. of the AAAI'99 Conference, 1999, pp. 439–446.Google Scholar
  11. [11]
    K. C. Gowda and G. Krishna, “Agglomerative clustering using the concept of mutual nearest neighborhood,” Pattern Recognition 10, 1978, 105–112.Google Scholar
  12. [12]
    A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice-Hall advanced reference series, Prentice-Hall, Inc., NJ, 1988.Google Scholar
  13. [13]
    R. A. Jarvis and E. A. Patrick, “Clustering using a similarity measure based on shared nearest neighbors,” IEEE Transactions on Computers C–22(11), 1973.Google Scholar
  14. [14]
    T. Joachims, D. Freitag and T. M. Mitchell, “Web watcher: A tour guide for the world wide web,” in Proc. Int. Joint Conf. on Artificial Intelligence, 1997, vol. 1, 770–777.Google Scholar
  15. [15]
    G. Karypis, “Evaluation of Item-Based Top-N Recommendation Algorithms,” in Proc. Int. Conf. on Information and Knowledge Management, 2001, pp. 247–254.Google Scholar
  16. [16]
    J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon and J. Riedl, “GroupLens: Applying collaborative filtering to usenet news,” Communications of the ACM 40(3), 1997, 77–87.Google Scholar
  17. [17]
    R. Kumar, P. Raghavan, S. Rajagopalan and A. Tomkins, “Recommendation systems: A probabilistic analysis,” Journal of Computer and System Sciences 63(1), 2001, 42–61.Google Scholar
  18. [18]
    R. D. Lawrence, G. Almasi, V. Kotlyar, M. Viveros and S. Duri, “Personalization of supermarket product recommendations,” Data Mining and Knowledge Discovery 5(1/2), 2001, 11–32.Google Scholar
  19. [19]
    H. Lieberman, “Letizia: An agent that assists web browsing,” in Proc. Int. Joint Conf. on Artificial Intelligence, 1995, vol. 1, 924–929.Google Scholar
  20. [20]
    W. Lin, S. A. Alvarez and C. Ruiz, “Efficient adaptive-support association rule mining for recommender systems,” Data Mining and Knowledge Discovery 6, 2002, 83–105.Google Scholar
  21. [21]
    C. D. Manning and H. Schuetze, Foundations of Statistical Natural Language Processing, MIT Press, 1999.Google Scholar
  22. [22]
    B. Mobasher, “Web usage mining and personalization,” Practical Handbook of Internet Computing, M. P. Singh (ed.), CRC Press, To appear 2004.Google Scholar
  23. [23]
    B. Mobasher, R. Cooley and J. Srivastava, “Automatic personalization based on web usage mining,” Communications of the ACM: Personalization 43(8), 2000, 142–151.Google Scholar
  24. [24]
    M. F. Moens, Automatic Indexing and Abstracting of Document Texts, Kluwer Academic Publishers, 2000.Google Scholar
  25. [25]
    F. Murtagh, “Complexities of hierarchic clustering algorithms: State of the art,” Computational Statistics Quarterly 1, 1984, 101–113.Google Scholar
  26. [26]
    M. N. Murty and G. Krishna, “A computationally efficient technique for data clustering,” Pattern Recognition 12, 1980, 153–158.Google Scholar
  27. [27]
    M. Nakagawa and B. Mobasher, “A hybrid web personalization model based on site connectivity,” in Workshop on Webmining as a Premise to Effective and Intelligent Web Applications (WebKDD'03), 2003.Google Scholar
  28. [28]
    M. Perkowitz and O. Etzioni, “Towards adaptive web sites: Conceptual Framework and Case Study,” Artificial Intelligence 118(1/2), 2000, 245–275.Google Scholar
  29. [29]
    B. M. Sarwar, G. Karypis, J. A. Konstan and J. Riedl, “Analysis of recommendation algorithms for e-commerce,” ACM Conference on Electronic Commerce 2000, 158–167.Google Scholar
  30. [30]
    B. M. Sarwar, G. Karypis, J. A. Konstan and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proc. 10th Int. Conf. on World Wide Web, 2001, pp. 285–295.Google Scholar
  31. [31]
    J. B. Schafer, J. A. Konstan and J. Riedl, “E-Commerce recommendation applications,” Data Mining and Knowledge Discovery 5(1/2), 2001, 115–153.Google Scholar
  32. [32]
    U. Shardanand and P. Maes, “Social information filtering: Algorithms for automatic word of mouth,” in Proc. Conf. on Human Factors in Computing Systems, 1995, pp. 210–217.Google Scholar
  33. [33]
    J. Srivastava, R. Cooley, M. Deshpande and P.-N. Tan, “Web usage mining: Discovery and applications of usage patterns from web data,” SIGKDD Explorations 1(2), 2000, 12–23.Google Scholar
  34. [34]
    K. J. Supowit, “The relative neighborhood graph, with an application to minimum spanning trees,” Journal of the ACM 30(3), 1983, 428–448.Google Scholar
  35. [35]
    A. Wexelblat and P. Maes, “Footprints: History-rich tools for information foraging,” in Proc. Conf. on Human Factors in Computing Systems, 1999, pp. 270–277.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Sergio Flesca
    • 1
  • Sergio Greco
    • 1
  • Andrea Tagarelli
    • 1
  • Ester Zumpano
    • 1
  1. 1.DEIS—Università della CalabriaRendeItaly

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