Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing remail: reinventing the email client through innovation and integration
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Understanding email use: predicting action on a message
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personalized Email Prioritization Based on Content and Social Network Analysis
IEEE Intelligent Systems
Information at your fingertips: contextual IR in enterprise email
Proceedings of the 16th international conference on Intelligent user interfaces
Should I open this email?: inbox-level cues, curiosity and attention to email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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We present Enterprise Priority Inbox Classifier (EPIC), an automatic personalized email prioritization system based on a topic-based user model built from the user's email data and relevant enterprise information. The user model encodes the user's topics of interest and email processing behaviors (e.g. read/reply/file) at the granularity of pair-wise interactions between the user and each of his/her email contacts. Given a new message, the user model is used in combination with the message metadata and content to determine the values of a set of contextual features. Contextual features include people-centric features representing information about the user's interaction history and relationship with the email sender, as well as message-centric features focusing on the properties of the message itself. Based on these feature values, EPIC uses a dynamic strategy to combine a global priority classifier with a user-specific classifier for determining the message's priority. An evaluation of EPIC based on 2,064 annotated email messages from 11 users, using 10-fold cross-validation, showed that the system achieves an average accuracy of 81.3%. The user-specific classifier contributed an improvement of 11.5%. Lastly we report on findings regarding the relative value of different contextual features for email prioritization.