The material recommendation system is practically useful for recommending (i, application-led material search ) a material that optimizes a specific property or (ii, material-led application search ) an application for a given material. To motivate an experimental measurement in the lab, it may be necessary to quantify the uncertainty associated with a property imputed by the recommendation system. We remark that one could achieve this through bootstrapping and training an ensemble of recommendation systems on the bootstrap samples of observed (material, property) values or more advanced matrix completion methods designed to quantify uncertainty .