Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
SWISH: semantic analysis of window titles and switching history
Proceedings of the 11th international conference on Intelligent user interfaces
The impact of task on the usage of web browser navigation mechanisms
GI '06 Proceedings of Graphics Interface 2006
A field study characterizing Web-based information-seeking tasks
Journal of the American Society for Information Science and Technology
Proceedings of the 1st Workshop on Context, Information and Ontologies
The CLOTHO project: predicting application utility
Proceedings of the 8th ACM Conference on Designing Interactive Systems
Activity recognition using eye-gaze movements and traditional interactions
Interacting with Computers
An approach to early recognition of web user tasks by the surfing behavior
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
Exploring the usability of web portals: A Croatian case study
International Journal of Information Management: The Journal for Information Professionals
Using file system content to organize e-mail
Proceedings of the 4th Information Interaction in Context Symposium
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The automatic identification of a user's task has the potential to improve information filtering systems that rely on implicit measures of interest and whose effectiveness may be dependant upon the task at hand. Knowledge of a user's current task type would allow information filtering systems to apply the most useful measures of user interest. We recently conducted a field study in which we logged all participants' interactions with their web browsers and asked participants to categorize their web usage according to a high-level task schema. Using the data collected during this study, we have conducted a preliminary exploration of the usefulness of logged web browser interactions to predict users' tasks. The results of this initial analysis suggest that individual models of users' web browser interactions may be useful in predicting task type.