Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Collaborative ordinal regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Introduction to the special issue on statistical and probabilistic methods for user modeling
User Modeling and User-Adapted Interaction
Using interest and transition models to predict visitor locations in museums
AI Communications - Recommender Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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
Using ontological and document similarity to estimate museum exhibit relatedness
Journal on Computing and Cultural Heritage (JOCCH)
Personalised rating prediction for new users using latent factor models
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
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
Computing similarity between items in a digital library of cultural heritage
Journal on Computing and Cultural Heritage (JOCCH)
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Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to predicting a user's interests (i. e., implicit item ratings) within a recommender system in the museum domain. We present the theoretical framework for a model based on Gaussian spatial processes, and discuss efficient algorithms for parameter estimation. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum, attaining a higher predictive accuracy than state-of-the-art collaborative filters.