Advertisement

A Flexible News Filtering Model Exploiting a Hierarchical Fuzzy Categorization

  • Gloria Bordogna
  • Marco Pagani
  • Gabriella Pasi
  • Robert Villa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4027)

Abstract

In this paper we present a novel news filtering model based on flexible and soft filtering criteria and exploiting a fuzzy hierarchical categorization of news. The filtering module is designed to provide news professionals and general users with an interactive and personalised tool for news gathering and delivery. It exploits content-based filtering criteria and category-based filtering techniques to deliver to the user a ranked list of either news or clusters of news. In fact, if the user prefers to have a synthetic view of the topics of recent news pushed by the stream, the system filters groups (clusters) of news having homogenous contents, identified automatically by the application of a fuzzy clustering algorithm that organizes the recent news into a fuzzy hierarchy. The filter can be trained explicitly by the user to learn his/her interests as well as implicitly by monitoring his/her interaction with the system. Several filtering criteria can be applied to select and rank news to the users based on the user’s information preferences and presentation preferences. User preferences specify what information (the contents of interest) is relevant to the user, the sources that provide reliable information, and the period of time during which the information remains relevant. Each individual news or cluster of news homogeneous with respect to their content is selected based on a customizable multi criteria decision making approach and ranked based on a combination of criteria specified by the user in his/her presentation preferences.

Keywords

Fuzzy Cluster User Profile News Story Collaborative Filter User Interest 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Amato, G., Straccia, U., Thanos, C.: EUROgatherer: a Personalised Gathering and Delivery Service on the Web. In: Proc. of the 4th SCI 2000 (2000)Google Scholar
  2. 2.
    Belkin, N.J., Croft, W.B.: Information filtering and Information Retrieval: Two sides of the same Coin? Communications of the ACM 35(12) (1992)Google Scholar
  3. 3.
    Bell, T.A.H., Moffat, A.: The Design of a High Performance Information Filtering System. In: SIGIR 1996, Zurich, Switzerland (1996)Google Scholar
  4. 4.
    Bordogna, G., Pagani, M., Pasi, G., Antoniolli, L., Invernizzi, F.: An Incremental Hierarchical Fuzzy Clustering Algorithm Supporting News Filtering. In: Proc. IPMU 2006, Paris (2006)Google Scholar
  5. 5.
    Bordogna, G., Pasi, G., Pagani, M., Villa, R.: PENG Filtering model and Demos. PENG Deliverable 3.1 (November 2005)Google Scholar
  6. 6.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-based and Collaborative Filters in an Online Newspaper. In: Proc. ACM SIGIR 1999 Workshop on Recommender Systems-Implementation and Evaluation (1999)Google Scholar
  7. 7.
    Connor, M., Herlocker, J.: Clustering for Collaborative Filtering. In: Proc. of ACM SIGIR Workshop on Recommender Systems (1999)Google Scholar
  8. 8.
    Crestani, F., Pasi, G. (eds.): Soft Computing in Information Retrieval: Techniques and Applications. Physica-Verlag, Heidelberg (2000)Google Scholar
  9. 9.
    Foltz, P.W., Dumais, S.T.: Personalized information delivery: an analysis of information filtering methods. Communications of the ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  10. 10.
    Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: Statistical semantics: An analysis of the potential performance of keyword information systems. Bell Syst. Tech. J. 62(6), 1753–1806 (1983)Google Scholar
  11. 11.
    Gabrilovich, S.D., Horvitz, E.: Newsjunkie: Providing Personalized Newsfeeds via Analysis of Information Novelty. In: WWW 2004, New York (2004)Google Scholar
  12. 12.
    Hathaway, R.J., Bezdek, J.C., Hu, Y.: Generalized Fuzzy C-Means Clustering Strategies Using Lp Norm Distances. IEEE Trans. on Fuzzy Systems 8(5), 576–582 (2000)CrossRefGoogle Scholar
  13. 13.
    Kilander F.: A brief comparison of News filtering Software, http://www.glue.umd.edu/enee/medlab/filter/filter.html
  14. 14.
    Kraft, D., Chen, J., Martin–Bautista, M.J., Vila, M.A.: Textual Information Retrieval with User Profiles using Fuzzy Clustering and Inferencing. In: Szczepaniak, P., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds.) Intelligent Exploration of the Web. Studies in Fuzziness and Soft Comp. Series, vol. 111, Physica Verlag, Heidelberg (2003)Google Scholar
  15. 15.
    Mackay, W.E., Malone, T.W., Crowston, K., Rao, R., Rosenblitt, D., Card, S.K.: How do experienced information lens user use rules? In: Proceedings of ACM CHI 1989 Conference on Human Factors in Computing Systems, Austin, Tex., April 30-May 4, pp. 211–216. ACM/SIGCHI, New York (1989)Google Scholar
  16. 16.
    Miyamoto, S.: Fuzzy IR and clustering techniques. Kluwer, Dordrecht (1990)Google Scholar
  17. 17.
    Oard, D.W., Marchionini, G.: A Conceptual Framework for Text Filtering, technical report EE-TR-96-25 CAR-TR-830 CLIS-TR-96-02 CS-TR-3643, University of Maryland (1996)Google Scholar
  18. 18.
    Pasi, G., Villa, R.: The PENG Project overview. In: IDDI-05-DEXA Workshop, Copenhagen (2005)Google Scholar
  19. 19.
    Robertson, S.E., Walker, S.: Okapi/Keenbow at TREC-8., In NIST Special Publication 500-246. The Eighth Text REtrieval Conference (TREC 8) (1999)Google Scholar
  20. 20.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval. McGraw-Hill, New York (1984)Google Scholar
  21. 21.
    Sollenborn, M., Funk, P.: Category-Based Filtering and User Stereotype Cases to Reduce the Latency Problem in Recommender Systems. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS, vol. 2416, p. 395. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Ungar, L.H., Foster, D.P.: Clustering Methods for Collaborative Filtering. In: Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park (1998)Google Scholar
  23. 23.
    Wong, W.-c., Fu, A.W.-c.: Incremental Document Clustering for Web Page Classification. In: Proc. Int. Conf. IS 2000, Aizu-Wakamatsu City, Japan (2000)Google Scholar
  24. 24.
    Yan, T.W., Garcia-Molina, H.: Index Structures for Information Filtering Under the Vector Space Model. In: Proc. 10th IEEE Int. Conf. on Data Engineering, Houston, pp. 337–347 (1994)Google Scholar
  25. 25.
    Zhang, Y., Callan, J., Minka, T.: Novelty and Redundancy Detection in Adaptive Filtering. In: Proc. of SIGIR 2002, Tampere, Finland (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gloria Bordogna
    • 1
  • Marco Pagani
    • 1
  • Gabriella Pasi
    • 2
  • Robert Villa
    • 2
  1. 1.CNR IDPADalmine (BG)Italy
  2. 2.Università degli Studi di Milano BicoccaMilanoItaly

Personalised recommendations