Title :
Email urgency reply prediction
Author :
Ayodele, Taiwo ; Shoniregun, Charles A. ; Zhou, Shikun
Author_Institution :
Res. Lab., Infonetmedia Ltd., Portsmouth, UK
Abstract :
The email urgency reply prediction (EURP) is a way of handling, and determining emails that require imperative reply with respect to time. Over the past few years, email has become the preferred medium of communication for many businesses and individuals. As a growing portion of our lives is captured over email exchanges, the phenomenon of the overcrowded and unmanaged inbox is becoming an increasingly serious impediment to communications and productivity. This research work focuses on the broader goal of providing users with effective applications to determine mails that require urgent replies - the task of urgency reply prediction has a sustainable economic benefits to both private and public sectors.
Keywords :
electronic mail; unsupervised learning; EURP; email determination; email handling; email machine learning; email urgency reply prediction; expectation learning; sustainable economic benefits; unsupervised machine learning; Indexes; email machine learning; expectation learning; keyword index; prediction; unsupervised machine learning;
Conference_Titel :
Information Society (i-Society), 2011 International Conference on
Conference_Location :
London
Print_ISBN :
978-1-61284-148-9