E-mail Categorization, Filtering, and Alerting on Mobile Devices: The ifMail Prototype and its Experimental Evaluation

  • Marco Cignini
  • Stefano Mizzaro
  • Carlo Tasso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2829)


We propose an integrated approach to email categorization, filtering, and alerting. After a general introduction to the problem, we present the ifMail prototype, capable of: categorize incoming email messages into pre-defined categories; filter and rank the categorized messages according to their importance; and alert the user on mobile devices when important messages are waiting to be read. The second part of the paper describes an extended evaluation of the ifMail prototype, whose results show the high effectiveness levels reached by the system.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., Spyropoulos, C.D.: An Evaluation of Naive Bayesian Anti-Spam Filtering. In: Proc. of the Workshop on Machine Learning in the New Information Age, 11th European Conference on Machine Learning( ECML), Barcelona, Spain, pp. 9–17 (2000)Google Scholar
  2. 2.
    Asnicar, F.A., Fant, M.D., Tasso, C.: User Model-Based Information Filtering. In: Lenzerini, M. (ed.) AI*IA 1997. LNCS, vol. 1321, pp. 242–253. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Brajnik, G., Tasso, C.: A shell for Developing Non-Monotonic User Modeling System. International Journal of Human-Computer Studies 40, 31–62 (1994)CrossRefGoogle Scholar
  4. 4.
    Brutlag, C., Meek, J.: Challenges of the email domain for text classification. In: Proc. of the 17th International Conference on Machine Learning, pp. 103-110 (2000)Google Scholar
  5. 5.
    Carreras, X., Marquez, L.: Boosting Trees for Anti-Spam Email Filtering. In: Proc. of RANLP 2001, 4th International Conference on Recent Advances in Natural Language Processing, Tzigovhark, BG (2001)Google Scholar
  6. 6.
    Cohen, W.: Learning Rules that Classify E-Mail, Papers from the AAAI Spring Symposium on Machine Learning in Information Access, pp. 18-25 (1996)Google Scholar
  7. 7.
    Crawford, E., Kay, J., McCreath, E.: Automatic Induction of Rules for e-mail classification. In: Proc. of the 6th Australian Document Computing Symposium. Coffs Harbour, Australia (2001)Google Scholar
  8. 8.
    Ducheneaut, N., Bellotti, V.: Email as Habitat. Interactions (September/October 2001)Google Scholar
  9. 9.
    Mackay, W.: Diversity in the Use of Electronic Mail: A Preliminary Inquiry. ACM Transactions on Office Information Systems 6(4), 380–397 (1988)CrossRefGoogle Scholar
  10. 10.
    Manco, G., Masciari, E., Ruffolo, M., Tagarelli, A.: Towards An Adaptive Mail Classifier. In: Atti dell’Ottavo Convegno AI*IA 2002, Siena, Italy, p. 63 (2002)Google Scholar
  11. 11.
    Minio, M., Tasso, C.: User Modellingfor Information Filteringon Internet Services: Exploiting an Extended Version of the UMT Shell. In: UM 1996 Workshop on User Modeling for Information Filtering on the WWW, Kaiula-Kona, Hawaii, USA (1996)Google Scholar
  12. 12.
    Minio, M., Tasso, C.: IFT: un’Interfaccia Intelligente per il Filtraggio di Informazioni Basato su Modellizzazione d’Utente. In: AI*IA Notizie, vol. IX(3), pp. 21–25 (1996)Google Scholar
  13. 13.
    Mizzaro, S., Tasso, C.: Ephemeral and Persistent Personalization in Adaptive Information Access to Scholarly Publications on theWeb. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 306–316. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Moulinier, I., Raskinis, G., Ganascia, J.G.: Text Categorization: a Symbolic Approach. In: Proc. of the 5th Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, pp. 87-99 (1996)Google Scholar
  15. 15.
    Pantel, P., Lin, D.: Spamcop: A spam classification & organization program. In: Proc. of AAAI 1998 Workshop on Learning for Text Categorization, pp. 95-98 (1998)Google Scholar
  16. 16.
    Payne, T., Edwards, P.: Interface agents that learn: An investigation of learning issues in a mail agent interface. Applied Artificial Intelligence 11, 1–32 (1997)CrossRefGoogle Scholar
  17. 17.
    Rennie, J.D.M.: ifile: An application of Machine Learning to E-Mail Filtering. In: Proc. KDD 2000 Workshop on Text Mining, Boston (2000)Google Scholar
  18. 18.
    Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)Google Scholar
  19. 19.
    Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  20. 20.
    Segal, R.B., Kephart, J.O.: Incremental Learning in SwiftFile. In: Proc. of the International Conference on Machine Learning, San Francisco, pp. 863-870 (2000)Google Scholar
  21. 21.
    Tasso, C., Armellini, M.: Exploiting User Modeling Techniques in Integrated Information Services: The TECHFINDER System. In: Proc. of the 6th AI*IA Congress, Bologna, I, September 14-17, pp. 519–522. Pitagora Editrice (1999)Google Scholar
  22. 22.
    Van Rijsbergen, K.: Information Retrieval, 2nd edn., Butterworths, London, UK (1979), http://www.dcs.gla.ac.uk/Keith/pdf
  23. 23.
    Venolia, G., Dabbish, L., Cadiz, J.J., Gupta, A.: Supporting Email Workflow. Microsoft Research Tech Report MSR-TR-2001-88 (2001)Google Scholar
  24. 24.
    Whittaker, S., Sidner, C.: Email Overload: ExploringP ersonal Information Management of Email. In: Proc. of the ACM CHI Conference, pp. 276-283 (1996)Google Scholar
  25. 25.
    Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proc. of the 22nd ACM SIGIR Conference, Berkley, CA, August 15-19, pp. 42–49 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Marco Cignini
    • 1
  • Stefano Mizzaro
    • 1
  • Carlo Tasso
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of UdineUdineItaly

Personalised recommendations