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 Task-Tracer users.
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Previous automatic task switch detection approaches have a high deviation in time of 3 to 5 minutes between predicted and actual task switches.
“or that one has to accept a high deviation in time of 3 to 5 minutes between predicted and actual task switches [13], [14], [18].”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [18]
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Using task context to improve programmer productivity
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Self-citationDetecting Developers' Task Switches and Types
Meyer, A. N., Satterfield, C., Zuger, M., Kevic, K., Murphy, G. C., Zimmermann, T., & Fritz, T. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2984086 · Cited by 14 · see citing works
“After the first version [18], they further improved their approach to reduce the number of false positives and to be able to predict task switches online [13], [14].”
BackgroundA Study on Automatic Classification of Users’ Desktop Interactions
Mirza, H. T., Chen, L., Hussain, I., Majid, A., & Chen, G. (2015) · Cybernetics and Systems · doi:10.1080/01969722.2015.1012372 · Cited by 8 · see citing works
BUILDING USER TASK SPACE BY MINING TEMPORALLY PROXIMATE DESKTOP ACTIONS
Mirza, H. T., Chen, L., Majid, A., & Chen, G. (2011) · Cybernetics and Systems · doi:10.1080/01969722.2011.634675 · Cited by 2 · see citing works
- Authors
- Shen, J., Li, L., Dietterich, T. G., & Herlocker, J. L.
- Affiliations
- Oregon State University (US)
- Venue
- Proceedings of the 11th international conference on Intelligent user interfaces
- Publisher
- ACM
- Published
- 2006-01-29
- Pages
- 86-92
- OpenAlex
- W2078786834
- Cited by
- 109
- References
- 18
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2021: 1 · 2020: 3 · 2019: 2 · 2017: 2 · 2016: 7 · 2015: 3 · 2014: 5 · 2013: 7 · 2012: 4
- FWCI
- 25.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- activity recognition, multitasking, feature selection, mutual information, Naive Bayes classifier, support vector machine, hybrid classifier
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