Daniel Lowd
ABSTRACT
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.
- W. Cohen, V. Carvalho, and T. Mitchell. Learning to classify email into speech acts. In Proc. Conf. Empirical Methods in Natural Language Processing, Barcelona, 2004.Google Scholar
- K. Crammer, O. Dekel, S. Shalev-shwartz, and Y. Singer. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7:2006, 2006. Google ScholarDigital Library
- A. N. Dragunov, T. G. Dietterich, K. Johnsrude, M. McLaughlin, L. Li, and J. L. Herlocker. Tasktracer: A desktop environment to support multi-tasking knowledge workers. In Proc. Int. Conf. on Intelligent User Interfaces, San Diego, CA, 2005. Google ScholarDigital Library
- M. Dredze and H. Wallach. User models for email activity management. In Workshop on Ubiquitous User Modeling, Int. Conf. Intelligent User Interfaces, 2008.Google Scholar
- Y. Huang, D. Govindaraju, T. Mitchell, V. Carvalho, and W. Cohen. Inferring ongoing activities of workstation users by clustering email. In Proc. Conf. Email and Anti-Spam, Mountain View, CA, 2004.Google Scholar
- T. Joachims. Making large-scale SVM learning practical, chapter 11. MIT Press, Cambridge, MA, 1999.Google Scholar
- T. Joachims. Learning to align sequences: A maximum-margin approach. Technical report, Cornell University, 2003.Google Scholar
- R. Khoussainov and N. Kushmerick. Email task management: An iterative relational learning approach. In Proc. Conf. Email and Anti-Spam, 2005.Google Scholar
- N. Kushmerick and T. Lau. Automated email activity management: An unsupervised learning approach. In Proc. Int. Conf. Intelligent User Interfaces, 2005. Google ScholarDigital Library
- D. C. Liu and J. Nocedal. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(3):503--528, 1989. Google ScholarDigital Library
- A. Mccallum. A comparison of event models for Naive Bayes text classification. In In AAAI-98 Workshop on Learning for Text Categorization, pages 41--48. AAAI Press, 1998.Google Scholar
- J. Shen, L. Li, T. Dietterich, and J. Herlocker. A hybrid learning system for recognizing user tasks from desk activities and email messages. In Proc. Int. Conf. Intelligent User Interfaces, pages 86--92, Sydney, Australia, 2006. Google ScholarDigital Library
- V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, NY, 1995. Google ScholarCross Ref
Index Terms
- Using salience to segment desktop activity into projects
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