For a fraction of observed values θ ∈ {0.1, 0.2, ..., 0.9}, we sampled an ensemble of COF-adsorption property matrices A (θ) (50 simulations of data collection for each θ). For each instance of A (θ) , we conducted a hyperparameter sweep using a training/validation split of the observed entries, retrained a deployment model on all observed entries, then tested the deployment model on the unobserved (missing) entries serving as test data. Fig. shows the distribution (among the simulations of data collection) of optimal hyperparameters (k (θ) opt , λ (θ) opt ) for each θ. Fig. shows Spearman's rank correlation coefficient, ρ, between the prediction of the missing adsorption property by the deployment low rank model and its actual value, for each adsorption property, as θ varies. The bands show the standard deviation over the 50 simulations of data collection. As in Fig. , ρ for the H 2 O Henry coefficient is much lower than for other properties ∀θ owing to its poor correlation with the other properties. For the majority of the gas adsorption properties, the recommendation system ranks COFs according to the property reasonably well (i.e., a reasonably high ρ), until θ is reduced below θ = 0.4, where the fraction of missing values is too large to provide accurate predictions owing to a paucity of training examples. In conclusion, at least 40 % of the values of the COF-adsorption-property matrix must be observed for the recommendation system to reliably rank COFs according to their adsorption properties. For comparison, ρ for the baseline material offset model is also shown in Fig. as the dashed line; the interaction term provides significant predictive value, with the exception of N 2 adsorption at (300 K, 0.001 bar) and O 2 adsorption at (298 K, 5 bar).