Email reply prediction: Unsupervised leaning approach

Taiwo Ayodele, Shikun Zhou

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


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 their email messages, we propose a new framework to help organised and prioritized email better; email reply prediction. 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.
Original languageEnglish
Title of host publication2008 Third International Conference on Digital Information Management
Number of pages6
ISBN (Print)978-1-4244-2916-5
Publication statusPublished - 13 Nov 2008
Externally publishedYes
EventThird International Conference on Digital Information Management ICDIM 2008 - University of East London, London, United Kingdom
Duration: 13 Nov 200816 Nov 2008


ConferenceThird International Conference on Digital Information Management ICDIM 2008
Abbreviated titleICDIM
Country/TerritoryUnited Kingdom

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