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)
Ontology-Based User Modeling in an Augmented Audio Reality System for Museums
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
Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
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
Aspect-Based Personalized Text Summarization
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Adaptive User Modelling and Recommendation in Constrained Physical Environments
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Predicting Customer Models Using Behavior-Based Features in Shops
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Personalized network updates: increasing social interactions and contributions in social networks
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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The vast amounts of information presented in museums can be overwhelming to a visitor, whose receptivity and time are typically limited. Hence, s/he might have difficulties selecting interesting exhibits to view within the available time. Mobile, context-aware guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and personalising the delivered content. The first step in this recommendation process is the accurate prediction of a visitor's activities and preferences. In this paper, we present two adaptive collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines their predictions. Our experimental results from a study using a small dataset of museum visits are encouraging, with the ensemble model yielding the best performance overall.