On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
The nature of statistical learning theory
The nature of statistical learning theory
Automated email activity management: an unsupervised learning approach
Proceedings of the 10th international conference on Intelligent user interfaces
TaskTracer: a desktop environment to support multi-tasking knowledge workers
Proceedings of the 10th international conference on Intelligent user interfaces
A hybrid learning system for recognizing user tasks from desktop activities and email messages
Proceedings of the 11th international conference on Intelligent user interfaces
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
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Knowledge workers must manage large numbers of simultaneous, ongoing projects that collectively involve huge numbers of resources (documents, emails, web pages, calendar items, etc). An activity database that captures the relationships among projects, resources, and time can drive a variety of tools that save time and increase productivity. To maximize net time savings, we would prefer to build such a database automatically, or with as little user effort as possible. In this paper, we present several sets of features and algorithms for predicting the project associated with each action a user performs on the desktop. Key to our methods is salience, the notion that more recent activity is more informative. By developing novel features that represent salience, we were able to learn models that outperform both a simple benchmark and an expert system tuned specifically for this task on real-world data from five users.