Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CityFlocks: designing social navigation for urban mobile information systems
Proceedings of the 7th ACM conference on Designing interactive systems
Reflecting human behavior to motivate desirable lifestyle
Proceedings of the 7th ACM conference on Designing interactive systems
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Augmented ethnography: designing a sensor-based toolkit for ethnographers
Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction
Hybrid user preference models for second life and opensimulator virtual worlds
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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Recommendation systems have become widespread, however these systems only determine information inputted from the customers through a browser, and cannot be used when actually moving around outside. This paper presents TTI Model, a model extracting individual's curiosity level in urban spaces on their spare time by collecting behavior data from sensors. It calculates person's real time curiosity level by analyzing behavior depending on the walking speed within the city, such as window shopping or just hanging around by themselves. This paper evaluates this model with a sensor device prototype, and elaborates possibilities when understanding individuals in detail, by extracting the curiosity predicted from current behaviors using sensors.