Fluid User Models For Personalized Mobile Apps

Augmenting media with helpful and interesting information has been the focus of various research projects in application areas ranging from collaborative work, where commentaries are added to documents by co-workers, to context and location based reminding applications, to reality augmentation, where images of reality are augmented with informative comments to help enhance the mobile user's experience. Tailoring the commentaries to fit the user's context and level of interest has been challenging. Most existing approaches to the modeling of user's interests and preferences cannot keep up with the minute-by-minute fluctuations in user focus and interests and are not sensitive enough to support or detect "serendipitous" items of interest. In this paper we describe a novel approach to constructing a user model. This model is inherently dynamic and is highly sensitive to the state of the user. In addition we describe BrightNotes (TM); a prototype system that demonstrates the fluidity of the model in the case of personalized augmentation of media such as movies, and conclude with a discussion of the applicability of fluid user models to location based services, augmented reality and other mobile apps.