Re-place-ing space: the roles of place and space in collaborative systems
CSCW '96 Proceedings of the 1996 ACM conference on Computer supported cooperative work
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
From awareness to repartee: sharing location within social groups
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
Discovering semantically meaningful places from pervasive RF-beacons
Proceedings of the 11th international conference on Ubiquitous computing
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Discovering human places of interest from multimodal mobile phone data
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Using decision-theoretic experience sampling to build personalized mobile phone interruption models
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Employing user feedback for semantic location services
Proceedings of the 13th international conference on Ubiquitous computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Evaluation methods and strategies for the interactive use of classifiers
International Journal of Human-Computer Studies
Capturing transitions between users' semantically meaningful places using mobile devices
Proceedings of the 1st ACM workshop on Mobile systems for computational social science
| Hi-index | 0.00 |
Recognising and learning users' semantically meaningful places is useful for personalising services and recommender systems, particularly in a mobile environment. Existing approaches that use mobile devices focus on automating place inference from underlying data, i.e. with little user interaction and intervention -- where user feedback is incorporated into the inference process. The process of intervention can be burdensome to the user but, without intervention, it is difficult to both capture personal place semantics and update places over time; resulting in a trade-off between system performance and user burden. In this paper, we present early results from a place recognition and learning approach that relies on user intervention as a form of active learning. Using simulations of user intervention generated from fine-grained ground truth, we show that good place semantic capture, classification and learning performance can feasibly be achieved in real time on mobile devices with only a small amount of user intervention.