Representation of electronic mail filtering profiles: a user study

ABSTRACT

Electronic mail offers the promise of rapid communication of essential information. However, electronic mail is also used to send unwanted messages. A variety of approaches can learn a profile of a user's interests for filtering mail. Here, we report on a usability study that investigates what types of profiles people would be willing to use to filter mail.

REFERENCES

• 1: Cohen, W. (1996). Learning Rules that Classify E-Mail In the 1996 AAAI Spring Symposium on Machine Learning in Information Access.
• 2: Duda, R. & Hart, P. (1973). Pattern classification and scene analysis. New York: John Wiley & Sons.
• 3: Susan Dumais , John Platt , David Heckerman , Mehran Sahami, Inductive learning algorithms and representations for text categorization, Proceedings of the seventh international conference on Information and knowledge management, p.148-155, November 02-07, 1998, Bethesda, Maryland, United States [doi>10.1145/288627.288651]
• 4: David D. Lewis , Robert E. Schapire , James P. Callan , Ron Papka, Training algorithms for linear text classifiers, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.298-306, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243277]
• 5: Michael Pazzani , Daniel Billsus, Learning and Revising User Profiles: The Identification ofInteresting Web Sites, Machine Learning, v.27 n.3, p.313-331, June 1997 [doi>10.1023/A:1007369909943]
• 6: Michael J. Pazzani, A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, v.13 n.5-6, p.393-408, Dec. 1999 [doi>10.1023/A:1006544522159]
• 7: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 8: Rocchio, J. (1911). Relevance feedback information retrieval. In Gerald Salton (editor). The SMART retrieval system- experiments in automated document processing (pp. 313-323). Prentice-Hall, Englewood Cliffs, NJ.
• 9: Sahami, M., Dumais, S., Heckerman, D. and E. Horvitz (1998). A Bayesian approach to filtering junk e-mail. AAAI'98 Workshop on Learning for Text Categorization, Madison, Wisconsin.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

General Terms:
Experimentation, Human Factors

Keywords:
mail filtering, user studies