Email reply prediction: a machine learning approach

Taiwo Ayodele, Shikun Zhou, Rinat Khusainov

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


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.
Original languageEnglish
Title of host publicationHuman Interface and the Management of Information. Information and Interaction
Subtitle of host publicationSymposium on Human Interface 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009, Proceedings, Part II
EditorsGavriel Salvendy, Michael J. Smith
PublisherSpringer Berlin
Number of pages10
ISBN (Electronic)978-3-642-02559-4
ISBN (Print)978-3-642-02558-7
Publication statusPublished - 19 Jul 2009
Externally publishedYes
EventSymposium on Human Interface 2009 - San Diego, United States
Duration: 19 Jul 200924 Jul 2009


ConferenceSymposium on Human Interface 2009
Country/TerritoryUnited States
CitySan Diego
Internet address

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