Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
Autonomous nondeterministic tour guides: improving quality of experience with TTD-MDPs
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Using interest and transition models to predict visitor locations in museums
AI Communications - Recommender Systems
Non-intrusive Personalisation of the Museum Experience
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Predicting user's movement with a combination of self-organizing map and markov model
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
A user-and item-aware weighting scheme for combining predictive user models
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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This paper proposes a personalised frequency-based model for predicting a user's pathway through a physical space, based on non-intrusive observations of users' previous movements Specifically, our approach estimates a user's transition probabilities between discrete locations utilising personalised transition frequency counts, which in turn are estimated from the movements of other similar users Our evaluation with a real-world dataset from the museum domain shows that our approach performs at least as well as a non-personalised frequency-based baseline, while attaining a higher predictive accuracy than a model based on the spatial layout of the physical museum space.