1. 3. User Study Simulate an average research work- day activity of reading a research paper while listening to music and being interrupted with conversations from neighbors. Subjects participates in four separate 45 minute sessions. A subject read an article in the first 30 minutes and answered article- related questions in the next 15 minutes. Hints related to the article or study genereated in the form of Visual, voice, visual and voice and void alarms. Subject provided feedback specifying the alarm-type she preferred. 6. Future Work 2. User-Context approach User-Centered Context Aware Framework for Application Personalization Anil Shankar, Sushil J Louis, Sergiu Dascalu Linda J.Hayes, Ramona Houmanfar {anilk, sushil, dascalus} @ cse.unr.edu {lhayes, ramonah} @ unr.nevada.educse.unr.edu Dept. of Computer Science & Engineering Dept. of Psychology University of Nevada, Reno, USA Acknowledgments: This work was supported in part by contract numbers: N00014-0301-0104 from the Office of Naval Research and National Science Foundation EPSCoR NSHE- 07-30 Current computer interfaces rely on the activity of an internal clock, keyboard and mouse to provide input/context to interact with a user. Reliance on such meager contextual information makes these interfaces unaware of a user and her environment. These context unaware user interfaces can only make weak attempts to adapt their behavior to individual user needs. 1. Motivation Harness external contextual information from a user’s environment (motion and speech) in addition to using contextual information internal to the computer. Use simple sensors to collect data on both the internal and external computer environments. Mine this contextual information for useful user-behavior patterns to better predict user preferences (behavior) and improve user interaction. 4. Experimental Design Talk, No-music Music, No-talk No-music, No-talk Music, Talk 0,3,2,1 1,0,2,3 3,0,1,2 Long- article 4 Talk, No-music Music, No-talk No-music, No-talk Music, Talk 1,3,0,2 2,3,1,0 2,0,3,1 3,2,0,1 Short- article 3 Talk, No-music Music, No-talk No-music, No-talk Music, Talk 3,1,0,2 1,0,2,3 1,3,2,0 3,0,2,1 Long- article 2 Talk, No-music Music, No-talk No-music, No-talk Music, Talk 0,2,3,1 3,1,2,0 2,1,0,3 Short- article 1 TreatmentAlarm Order* TaskSession * 0 = No-alarm; 1 = Visual-alarm; 2 = Voice- Alarm; 3 = Voice and visual alarms 1.XCS has an average performance of 90 percent 2.XCS outperforms 1R and J48 for 8/10 users on the 2Class problem and 9/10 users on the 4Class problem. 3.Removing external user-context degrades XCS’ and J48’s performance 4.Removing external user-context (motion and speech) degrades the alarm-predictive accuracy 5.One-R’s tree picks user-identity attribute, that is knowing who the user is helps the calendar-interface to personalize its alarm-type to that particular user 6.Decision-trees examined show that user-context features like user-id, motion-information are critical for high prediction accuracy Incorporate open-source applications (Sunbird, xmms) into our framework and enable them to be user-context aware Distribute the software and collect short-term and long-term data from a diverse group of users XCS Prediction Accuracy 5. Results