We demonstrated a COF recommendation system for different gas adsorption applications. Our COF-adsorption-property matrix was composed of the simulated uptake of several gases at different conditions in 560 COF structures by Ongari et al. . We simulated the process of data observation by artificially introducing missing values into the matrix. The (simulated) unobserved entries served as test data to assess the performance of the data imputation by the recommendation system. To both (i) impute the missing adsorption properties and (ii) machine-learn a "map" of COFs, wherein COFs with similar adsorption properties congregate, we trained a low rank matrix model of the COF-adsorption-property matrix that had missing entries. The recommendation system was able to rank COFs according to their adsorption properties reasonably well (Spearman's rank correlation coefficient > 0.6), with the exception of water Henry coefficients. Moreover, coloring of the learned map of COFs by the adsorption properties indicated that, indeed, COFs with similar (dissimilar) adsorption properties clustered together (separated) in the map. The imputation performance of the recommendation system precipitously drops once the fraction of missing entries exceeds 60 %, though this figure does not necessarily generalize to other data sets.