The perception: a probabilistic model for information storage and organization in the brain
Neurocomputing: foundations of research
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Taking email to task: the design and evaluation of a task management centered email tool
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
UMEA: translating interaction histories into project contexts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A diary study of task switching and interruptions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
Fewer clicks and less frustration: reducing the cost of reaching the right folder
Proceedings of the 11th international conference on Intelligent user interfaces
Activity explorer: activity-centric collaboration from research to product
IBM Systems Journal
Matching attentional draw with utility in interruption
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Real-time detection of task switches of desktop users
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
From documents to tasks: deriving user tasks from document usage patterns
Proceedings of the 15th international conference on Intelligent user interfaces
Assisting engineers in switching artifacts by using task semantic and interaction history
Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
Studying the factors influencing automatic user task detection on the computer desktop
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
FolderPredictor: Reducing the cost of reaching the right folder
ACM Transactions on Intelligent Systems and Technology (TIST)
Which version is this?: improving the desktop experience within a copy-aware computing ecosystem
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic detection of accommodation steps as an indicator of knowledge maturing
Interacting with Computers
Evaluation of social media collaboration using task-detection methods
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
Switch detector: an activity spotting system for desktop
Proceedings of the 20th ACM international conference on Information and knowledge management
Annotating knowledge work lifelog: term extraction from sensor and operation history
Proceedings of the 20th ACM international conference on Information and knowledge management
Twitter, sensors and UI: robust context modeling for interruption management
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Automatically detecting developer activities and problems in software development work
Proceedings of the 34th International Conference on Software Engineering
Automatic and continuous user task analysis via eye activity
Proceedings of the 2013 international conference on Intelligent user interfaces
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The TaskTracer system allows knowledge workers to define a set of activities that characterize their desktop work. It then associates with each user-defined activity the set of resources that the user accesses when performing that activity. In order to correctly associate resources with activities and provide useful activity-related services to the user, the system needs to know the current activity of the user at all times. It is often convenient for the user to explicitly declare which activity he/she is working on. But frequently the user forgets to do this. TaskTracer applies machine learning methods to detect undeclared activity switches and predict the correct activity of the user. This paper presents TaskPredictor2, a complete redesign of the activity predictor in TaskTracer and its notification user interface. TaskPredictor2 applies a novel online learning algorithm that is able to incorporate a richer set of features than our previous predictors. We prove an error bound for the algorithm and present experimental results that show improved accuracy and a 180-fold speedup on real user data. The user interface supports negotiated interruption and makes it easy for the user to correct both the predicted time of the task switch and the predicted activity.