A hybrid learning system for recognizing user tasks from desktop activities and email messages

The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while carrying out their work activities. It does this by associating with each user-defined activity the set of files, folders, email messages, contacts, and web pages that the user accesses when performing that activity. The initial TaskTracer system relies on the user to notify the system each time the user changes activities. However, this is burdensome, and users often forget to tell TaskTracer what activity they are working on. This paper introduces TaskPredictor, a machine learning system that attempts to predict the user’s current activity. TaskPredictor has two components: one for general desktop activity and another specifically for email. TaskPredictor achieves high prediction precision by combining three techniques: (a) feature selection via mutual information, (b) classification based on a confidence threshold, and (c) a hybrid design in which a Naive Bayes clas...

• Type: Conference
• Year: 2006
• Where: IUI
• Authors: Jianqiang Shen, Lida Li, Thomas G. Dietterich, Jonathan L. Herlocker
• Keywords: General Desktop Activity, IUI 2006, Software Engineering, User-defined Activity, User’s Current Activity