CORE employs a matrix factorization model based on factorization machines and the open-world assumption to capture latent semantic information about the individual variables.
we associate with each variable v ∈ V a bias term b v ∈ R and a latent feature vector f v ∈ R d , where the dimensionality d of the latent feature space is a hyperparameter of our model.