Using Gaussian Spatial Processes to Model and Predict Interests in Museum Exhibits.

This paper adapts models from the area of spatial statistics to the task of predicting a user's interests (i. e., implicit item ratings) within a recommender system in the museum domain. We develop a model based on Gaussian spatial processes, and discuss two ways of computing item-to-item distances in the museum setting. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum. Overall, our model attains a higher predictive accuracy than nearest-neighbour collaborative filters. In addition, the model variant using physical distances outperforms that using distances computed from item-to-item similarities.