Herein, we demonstrate a prototype recommendation system, based on a low rank matrix model , that recommends COFs for various gas adsorption tasks. The COF-gas-adsorption-property matrices pertain to 560 experimentally-reported COFs and the simulated uptakes of CH 4 , H 2 O, H 2 S, Xe, Kr, CO 2 , N 2 , O 2 and H 2 in those COFs at various conditions relevant to different gas storage and separation applications. Advantageously, this COF-gas-adsorption-property matrix is in reality complete, allowing us to ablate different fractions of the entries and investigate how imputation performance depends on the fraction of missing valvues. From the observed (COF, gas adsorption) values, we machine-learn a low rank model, giving low-dimensional latent vector representations of both the COFs and the adsorption properties, allowing (i) accurate imputation of the missing values of the adsorption properties and (ii) the embedding of the COFs, represented as points, into a "map" wherein COFs with similar (dissimilar) adsorption properties congregate (separate). Such a map of COFs is useful for experimental design to explore COF space and for the optimization of promising but still suboptimal "lead"-COFs.