Map-Based Recommendation of Hyperlinked Document Collections

  • Mieczysław A. Kłopotek
  • Sławomir T. Wierzchoń
  • Krzysztof Ciesielski
  • Michał Dramiński
  • Dariusz Czerski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4082)


The increasing number of documents returned by search engines for typical requests makes it necessary to look for new methods of representation of the search results.

In this paper, we discuss the possibility to exploit incremental, navigational maps based both on page content, hyperlinks connecting similar pages and ranking algorithms (such as HITS, SALSA, PHITS and PageRank) in order to build visual recommender system. Such system would have an immediate impact on business information management (e.g. CRM and marketing, consulting, education and training) and is a major step on the way to information personalization.


Recommender System Active Object Document Cluster Probabilistic Latent Semantic Analysis Node Utility 
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.
    Aggarwal, C.C., Al-Garawi, F., Yu, P.S.: Intelligent crawling on the World Wide Web with arbitrary predicates. In: Proc. 10th Int. World Wide Web Conference, pp. 96–105 (2001)Google Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie, D.C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  3. 3.
    Callan, J., et al.: Personalisation and recommender systems in digital libraries, Joint NSF-EU DELOS Working Group Report (May 2003),
  4. 4.
    Cayzer, S., Aickelin, U.: A Recommender System based on Idiotypic Artificial Immune Networks. J. of Mathematical Modelling and Algorithms 4(2), 181–198 (2005)MATHCrossRefGoogle Scholar
  5. 5.
    Ciesielski, K., et al.: Adaptive document maps. In: Proc. IIPWM 2006. Springer, Heidelberg (2006)Google Scholar
  6. 6.
    Cohn, D., Chang, H.: Learning to probabilistically identify authoritative documents. In: Proceedings of the 17th International Conference on Machine Learning (2000)Google Scholar
  7. 7.
    Berry, M.W.: Large scale singular value decompositions. Int. Journal of Supercomputer Applications 6(1), 13–49 (1992)Google Scholar
  8. 8.
    Decker, R.: Identifying patterns in buying behavior by means of growing neural gas network. In: Operations Research Conference, Heidelberg (2003)Google Scholar
  9. 9.
    Dittenbach, M., Rauber, A., Merkl, D.: Discovering hierarchical structure in data using the growing hierarchical Self-Organizing Map. Neurocomputing 48(1-4), 199–216 (2002)MATHCrossRefGoogle Scholar
  10. 10.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems 7, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  11. 11.
    Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Proc. of the Int. Conference on Artificial Neural Networks 1997, pp. 613–618 (1997)Google Scholar
  12. 12.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communication of the ACM 35, 61–70 (1992)CrossRefGoogle Scholar
  13. 13.
    Hoffmann, T.: Probabilistic latent semantic analysis. In: Proceedings of the 15th Conference on Uncertainty in AI (1999)Google Scholar
  14. 14.
    Hung, C., Wermter, S.: A constructive and hierarchical self-organising model in a non-stationary environment. In: Int. Joint Conference in Neural Networks (2005)Google Scholar
  15. 15.
    Jameson, A.: More than the sum of its Mmmbers: Challenges for group recommender. In: Proc. of the Int. Working Conference on Advanced Visual Interfaces, Gallipoli, Italy (2004),
  16. 16.
    Kłopotek, M.: A new Bayesian tree learning method with reduced time and space complexity. Fundamenta Informaticae 49(4), 349–367 (2002)MATHMathSciNetGoogle Scholar
  17. 17.
    Kłopotek, M., Dramiński, M., Ciesielski, K., Kujawiak, M., Wierzchoń, S.T.: Mining document maps. In: Gori, M., Celi, M., Nanni, M. (eds.) Proceedings of Statistical Approaches to Web Mining Workshop (SAWM) at PKDD 2004, Pisa, pp. 87–98 (2004)Google Scholar
  18. 18.
    Kłopotek, M., Wierzchoń, S.T., Ciesielski, K., Dramiński, M., Kujawiak, M.: Coexistence of fuzzy and crisp concepts in document maps. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS (LNAI), vol. 3697, pp. 859–864. Springer, Heidelberg (2005)Google Scholar
  19. 19.
    Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)MATHGoogle Scholar
  20. 20.
    Livesay, B.C.K., Lund, K.: Explorations in context space: Words, sentences, discourse. Discourse Processes 25(2-3), 211–257 (1998)Google Scholar
  21. 21.
    Sarwar, G., Karypis, J., Konstan, J.: Riedl: Item-based Collaborative Filtering Recommendation Algorithms. In: WWW10, Hong Kong, May 1-5 (2001)Google Scholar
  22. 22.
    Schafer, J.B., Konstan, J., Riedl, J.: Electronic Commerce Recommender Applications. Journal of Data Mining and Knowledge Discovery 5(1-2), 115–152 (2001)MATHCrossRefGoogle Scholar
  23. 23.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating ”word of mouth”. In: ACM Conference Proceedings on Human Factors in Computing Systems, Denver, CO, May 7-11, pp. 210–217 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mieczysław A. Kłopotek
    • 1
  • Sławomir T. Wierzchoń
    • 1
  • Krzysztof Ciesielski
    • 1
  • Michał Dramiński
    • 1
  • Dariusz Czerski
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarszawaPoland

Personalised recommendations