The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Identifying Meaningful Places: The Non-parametric Way
Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning time-based presence probabilities
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Finding your friends and following them to where you are
Proceedings of the fifth ACM international conference on Web search and data mining
Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Improving location prediction services for new users with probabilistic latent semantic analysis
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
| Hi-index | 0.00 |
In recent years, researchers have sought to capture the daily life location behaviour of groups of people for exploratory, inference, and predictive purposes. However, development of such approaches has been limited by the requirement of personal semantic labels for locations or social/spatial overlap between individuals in the group. To address this shortcoming, we present a Bayesian model of mobility in populations (i.e., groups without spatial or social interconnections) that is not subject to any of these requirements. The model intelligently shares temporal parameters between people, but keeps the spatial parameters specific to individuals. To illustrate the advantages of population modelling, we apply our model to the difficult problem of overcoming data sparsity in location prediction systems, using the Nokia dataset comprising 38 individuals, and find a factor of 2.4 improvement in location prediction performance against a state-of-the-art model when training on only 20 hours of observations.