Fluid user models for personalized mobile apps Overview of the BrightNotes™ system

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™; 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.

[1]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[2]  Geoffrey W. Sutton.  Stumbling on Happiness , 2008 .

[3]  Euthimios Panagos,et al.  Information filtering and personalization: Context, serendipity and group profile effects , 2011, 2011 IEEE Consumer Communications and Networking Conference (CCNC).

[4]  Eric Horvitz,et al.  Models of attention in computing and communication , 2003, Commun. ACM.

[5]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[6]  Gert R. G. Lanckriet,et al.  Smarter than Genius? Human Evaluation of Music Recommender Systems , 2009, ISMIR.

[7]  Shoshana Loeb,et al.  Architecting personalized delivery of multimedia information , 1992, CACM.

[8]  Euthimios Panagos,et al.  The Fabric of Mobile Services , 2009 .

[9]  Ritu Chadha,et al.  Automated Policy Analysis , 2012, 2012 IEEE International Symposium on Policies for Distributed Systems and Networks.

[10]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[11]  Rosalind W. Picard.  Affective computing: challenges , 2003, Int. J. Hum. Comput. Stud..