Email Reply Prediction: A Machine Learning Approach

  • Taiwo Ayodele
  • Shikun Zhou
  • Rinat Khusainov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5618)


Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.


Email reply prediction machine learning email messages interrogative words need reply do not need reply email headers unsupervised learning 


  1. Whittaker, S., Sider, C.: Email overload: exploring personal information management of email. In: CHI 1996, pp. 276–283. ACM Press, New York (1996)Google Scholar
  2. Tyler, J., Tang, J.: When can I expect an email response? A study of rhythms in email usage. In: Proc. of ECSCW 2003, pp. 238–258. Oulu Univ. Press (2003)Google Scholar
  3. Dredze, M., Blitzer, J., Pereira, F.: Reply Expectation Prediction for Email Management. In: CEAS 2005, 2nd Conference on Email and Anti-Spam, Stanford University, CA (2005)Google Scholar
  4. Salton, G., Wong, A., Yang, S.S.: A vector space model for automatic indexing. Communications of the ACM 18, 613–620 (2005)CrossRefzbMATHGoogle Scholar
  5. Ducheneaut, N., Belloti, V.: Email as habitat: An exploration of embedded personal information management. Interactions 8(5), 30–38 (2001)CrossRefGoogle Scholar
  6. Kraut, R.E., Attewell, P.: Media use in a global corporation: Electronic mail and organizational knowledge, in Culture of the Internet, pp. 323–342. Lawrence Erlbaum Associates, Mahwah (1997)Google Scholar
  7. 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
  8. Sproull, L., Kiesler, S.: Connections: New ways of working in the networked organization. MIT Press, Cambridge (1991)Google Scholar
  9. Klimt, B., Yang, Y.: The Enron corpus: A new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS, vol. 3201, pp. 217–226. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Taiwo Ayodele
    • 1
  • Shikun Zhou
    • 1
  • Rinat Khusainov
    • 1
  1. 1.Department of Electronics and Computer EngineeringUniversity of PortsmouthPortsmouthUnited Kingdom

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