Here, we formulate the general problem of material-property matrix completion. A material recommendation system jointly machine-learns, from observed (material, property) values, low-dimensional latent vector representations of the materials and properties that express (material, property) affinities. These learned representations allow us to (i) impute the missing (material, property) values and (ii) draw a map of the materials, wherein materials with similar properties congregate.