Email Reply Prediction: A Machine Learning Approach

Authors:
Taiwo Ayodele;Shikun Zhou;Rinat Khusainov
Affiliations:
Department of Electronics and Computer Engineering, University of Portsmouth, Portsmouth, United kingdom PO1 3DJ;Department of Electronics and Computer Engineering, University of Portsmouth, Portsmouth, United kingdom PO1 3DJ;Department of Electronics and Computer Engineering, University of Portsmouth, Portsmouth, United kingdom PO1 3DJ
Venue:
Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009
Year:
2009

Citing 6
Cited 0

Diversity in the use of electronic mail: a preliminary inquiry

ACM Transactions on Information Systems (TOIS)
Connections: new ways of working in the networked organization

Connections: new ways of working in the networked organization
Email overload: exploring personal information management of email

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A vector space model for automatic indexing

Communications of the ACM
E-mail as habitat: an exploration of embedded personal information management

interactions
When can i expect an email response? a study of rhythms in email usage

ECSCW'03 Proceedings of the eighth conference on European Conference on Computer Supported Cooperative Work

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.