Spatial Processes for Recommender Systems

Fabian Bohnert, Daniel Francis Schmidt, Ingrid Zukerman

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.