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
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Activity Inference through Sequence Alignment
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Routine classification through sequence alignment
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Learning and predicting multimodal daily life patterns from cell phones
Proceedings of the 2009 international conference on Multimodal interfaces
Predicting human behaviour from selected mobile phone data points
Proceedings of the 12th ACM international conference on Ubiquitous computing
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
User experience of social ad hoc networking: findings from a large-scale field trial of TWIN
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
By their apps you shall understand them: mining large-scale patterns of mobile phone usage
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Pervasive sensing to model political opinions in face-to-face networks
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Understanding mobile web and mobile search use in today's dynamic mobile landscape
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
Artificial Intelligence
User-dependent aspect model for collaborative activity recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Getting real: a naturalistic methodology for using smartphones to collect mediated communications
Advances in Human-Computer Interaction
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Towards a semi-automatic personal digital diary: detecting daily activities from smartphone sensors
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Generating tourism path from trajectories and geo-photos
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
Improving activity recognition without sensor data: a comparison study of time use surveys
Proceedings of the 4th Augmented Human International Conference
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Using time use with mobile sensor data: a road to practical mobile activity recognition?
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68,000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA), automatically discovers characteristic routines for all individuals in the study, including "going to work at 10am", "leaving work at night", or "staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as "being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines.