C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Beyond Interaction Design: Beyond Human-Computer Interaction
Beyond Interaction Design: Beyond Human-Computer Interaction
Predicting human interruptibility with sensors: a Wizard of Oz feasibility study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning and reasoning about interruption
Proceedings of the 5th international conference on Multimodal interfaces
Examining the robustness of sensor-based statistical models of human interruptibility
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
If not now, when?: the effects of interruption at different moments within task execution
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
BusyBody: creating and fielding personalized models of the cost of interruption
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Classifier fitness based on accuracy
Evolutionary Computation
Human-Computer Interaction
XCS for adaptive user-interfaces
Proceedings of the 9th annual conference on Genetic and evolutionary computation
XCS for personalizing desktop interfaces
IEEE Transactions on Evolutionary Computation
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We present results from an empirical user-study with ten users which investigates if information from a user's environment helps a user interface to personalize itself to individual users to better meet usability goals and improve user-experience. In our research we use a microphone and a web-camera to collect this information (user-context) from the vicinity of a subject's desktop computer. Sycophant, our context-aware calendaring application and research test-bed uses machine learning techniques to successfully predict a user-preferred alarm type. Discounting user identity and motion information significantly degrades Sycophant's performance on the alarm prediction task. Our user study emphasizes the need for user-context for personalizable user interfaces which can better meet effectiveness and utility usability goals. Results from our study further demonstrate that contextual information helps adaptive interfaces to improve user-experience.