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

ABSTRACT

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 classifier estimates the classification confidence but where the actual classification decision is made by a support vector machine. This paper provides experimental results on data collected from TaskTracer users.

REFERENCES

• 1: Y. Altun, I. Tsochantaridis, and T. Hofmann. Hidden Markov support vector machines. In Proc. of ICML-03.
• 2: S. Andrews, L. Cai, D. Gondek, A. Greenwald, D. Grollman, A. M. Jonsson, K. Hall, M. Lease, B. Ng, J. Raiti, V. Sweetser, and J. Turner. Astrology: the study of astro teller. In ICML04 Workshop Physiological Data Modeling - A Competition, 2004.
• 3: Jake D. Brutlag , Christopher Meek, Challenges of the Email Domain for Text Classification, Proceedings of the Seventeenth International Conference on Machine Learning, p.103-110, June 29-July 02, 2000
• 4: W. W. Cohen. Learning rules that classify e-mail. In Proc. Of the 1996 AAAI Spring Symposium in Information Access, 1996.
• 5: Anton N. Dragunov , Thomas G. Dietterich , Kevin Johnsrude , Matthew McLaughlin , Lida Li , Jonathan L. Herlocker, TaskTracer: a desktop environment to support multi-tasking knowledge workers, Proceedings of the 10th international conference on Intelligent user interfaces, January 10-13, 2005, San Diego, California, USA [doi>10.1145/1040830.1040855]
• 6: Tom Fawcett , Foster Provost, Activity monitoring: noticing interesting changes in behavior, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.53-62, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312195]
• 7: K. Haigh and H. A. Yanco. Automation as caregiver: a survey of issues and technologies. In AAAI workshop on Automation as Caregiver, 2002.
• 8: E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Proc. of UAI-98, 1998.
• 9: Eric Horvitz , Johnson Apacible, Learning and reasoning about interruption, Proceedings of the 5th international conference on Multimodal interfaces, November 05-07, 2003, Vancouver, British Columbia, Canada [doi>10.1145/958432.958440]
• 10: E. Horvitz, A. Jacobs, and D. Hovel. Attention-sensitive alerting. In Proc. of UAI-99, 1999.
• 11: Thorsten Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer Academic Publishers, Norwell, MA, 2002
• 12: B. Klimt and Y. Yang. The enron corpus: A new dataset for email classification research. In Proc. of ECML2004, 2004.
• 13: A. McCallum and G. Huang. Automatic categorization of email into folders: Benchmark experiments on enron and sri corpora. Technical Report IR-418, CIIR, 2004.
• 14: M. Philipose, K. Fishkin, M. Perkowitz, D. Patterson, and D. Hahnel. The probabilistic activity toolkit: Towards enabling activity-aware computer interfaces. Technical Report IRS-TR-03-013, Intel Research Lab, Seattle, WA, 2003.
• 15: M. Porter. An algorithm for suffix stripping. Program, 14(3):130--137, 1980.
• 16: Richard B. Segal , Jeffrey O. Kephart, MailCat: an intelligent assistant for organizing e-mail, Proceedings of the third annual conference on Autonomous Agents, p.276-282, April 1999, Seattle, Washington, United States [doi>10.1145/301136.301209]
• 17: Padhraic Smyth , David Heckerman , Michael I. Jordan, Probabilistic independence networks for hidden Markov probability models, Neural Computation, v.9 n.2, p.227-269, Feb. 15, 1997 [doi>10.1162/neco.1997.9.2.227]
• 18: T.-F. Wu, C.-J. Lin, and R. C. Weng. Probability estimates for multi-class classification by pairwise coupling. In Advances in NIPS 16.
• 19: Yiming Yang , Jan O. Pedersen, A Comparative Study on Feature Selection in Text Categorization, Proceedings of the Fourteenth International Conference on Machine Learning, p.412-420, July 08-12, 1997

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Office automation

General Terms:
Design, Experimentation, Human Factors

Keywords:
intelligent interfaces, machine learning, naive Bayes, support vector machines