AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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Spatial Processes for Recommender Systems
Fabian Bohnert, Daniel F. Schmidt, Ingrid Zukerman

Last modified: 2009-06-26

Abstract


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.

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