The Journal of Machine Learning Research
Sensing and Modeling Human Networks using the Sociometer
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Rhythm modeling, visualizations and applications
Proceedings of the 16th annual ACM symposium on User interface software and technology
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning and inferring transportation routines
Artificial Intelligence
Periodic properties of user mobility and access-point popularity
Personal and Ubiquitous Computing
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Predicting human behaviour from selected mobile phone data points
Proceedings of the 12th ACM international conference on Ubiquitous computing
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
An unsupervised learning approach to social circles detection in ego bluetooth proximity network
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
We know how you live: exploring the spectrum of urban lifestyles
Proceedings of the first ACM conference on Online social networks
A probabilistic approach to mining mobile phone data sequences
Personal and Ubiquitous Computing
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Human behavior understanding is a fundamental problem in many ubiquitous applications. It aims to automatically uncover and quantify characteristic behavior patterns in users' daily lives as well as disclose behavior clustering structure among multiple users. The key challenge is how to define a naturally interpreted representation for users' daily behavior patterns, which can be easily exploited to not only uncover the behavior similarity among multiple users but also predict users' future activities. In this paper, we define such a representation, and propose a probabilistic framework which can automatically learn it from mass amount of mobile data in unsupervised setting and exploit it to predict user activities. By an appropriate information sharing among multiple users, this framework overcomes single-user data sparsity problem and effectively identifies behavior clustering structures in a set of users. Experiments conducted on a public reality mining data set demonstrate the effectiveness and accuracy of our methods.