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.

Authors

• Fabian Bohnert
• Daniel F. Schmidt
• Ingrid Zukerman Richard McConachy

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