Communications of the ACM
Activity-based Information Retrieval: Technology in Support of Personal Memory
Proceedings of the IFIP 12th World Computer Congress on Personal Computers and Intelligent Systems - Information Processing '92 - Volume 3 - Volume 3
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Rhythm modeling, visualizations and applications
Proceedings of the 16th annual ACM symposium on User interface software and technology
IEEE Transactions on Knowledge and Data Engineering
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
A diary study of mobile information needs
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
Activity duration analysis for context-aware services using foursquare check-ins
Proceedings of the 2012 international workshop on Self-aware internet of things
An approach to social recommendation for context-aware mobile services
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Designing for video: investigating the contextual cues within viewing situations
Personal and Ubiquitous Computing
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Today's mobile leisure guide systems give their users unprecedented help in finding places of interest. However, the process still requires significant user interaction, for example to specify preferences and navigate lists. While interaction is effective for obtaining desired results, learning the interaction pattern can be an obstacle for new users, and performing it can slow down experienced users. This paper describes how to infer a user's high-level activity automatically to improve recommendations. Activity is determined by interpreting a combination of current sensor data, models generated from historical sensor data, and priors from a large time-use study. We present an initial user study that shows an increase in prediction accuracy from 62% to over 77%, and discuss the challenges of integrating activity representations into a user model.