Spatial Processes for Recommender Systems

Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to the prediction of a user’s interests or item ratings in recommender systems. 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 simulated data and a real-world dataset collected by tracking visitors in a museum, and achieves a higher predictive accuracy than a non-personalised baseline. Additionally, in the real-world scenario, the model attains a higher predictive accuracy than state-of-the-art collaborative filters.