An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The role of adaptive hypermedia in a context-aware tourist GUIDE
Communications of the ACM - The Adaptive Web
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Consistent Modelling of Users, Devices and Sensors in a Ubiquitous Computing Environment
User Modeling and User-Adapted Interaction
Ontology-Based User Modeling in an Augmented Audio Reality System for Museums
User Modeling and User-Adapted Interaction
Preface to the Special Issue on User Modeling in Ubiquitous Computing
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
A hybrid approach for improving predictive accuracy of collaborative filtering algorithms
User Modeling and User-Adapted Interaction
Introduction to the special issue on statistical and probabilistic methods for user modeling
User Modeling and User-Adapted Interaction
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
Using viewing time for theme prediction in cultural heritage spaces
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
CHIP demonstrator: semantics-driven recommendations and museum tour generation
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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
Assessing the Impact of Measurement Uncertainty on User Models in Spatial Domains
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Spatial processes for recommender systems
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using Keyword-Based Approaches to Adaptively Predict Interest in Museum Exhibits
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Using ontological and document similarity to estimate museum exhibit relatedness
Journal on Computing and Cultural Heritage (JOCCH)
A visitor's guide in an active museum: Presentations, communications, and reflection
Journal on Computing and Cultural Heritage (JOCCH)
Towards the prediction of user actions on exercises with hints based on survey results
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
Personalised pathway prediction
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Personalization in cultural heritage: the road travelled and the one ahead
User Modeling and User-Adapted Interaction
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Museums offer vast amounts of information, but a visitor's receptivity and time are typically limited, providing the visitor with the challenge of selecting the (subjectively) interesting exhibits to view within the available time. Mobile, electronic handheld guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and adapting the delivered content. The first step in this personalisation process is the prediction of a visitor's activities and interests. In this paper we study non-intrusive, adaptive user modelling techniques that take into account the physical constraints of the exhibition layout. We present two collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines the predictions of these models. The three models were trained and tested on a small dataset of museum visits. Our results are encouraging, with the ensemble model yielding the best performance overall.