As a baseline to judge the performance of our recommendation system, we also train and test (on the same data) a benchmark model that excludes the interaction term m m p p in eqn. 1. This material bias model, equivalent to the low rank model in eqn. 2 when k = 0, gives A mp ≈ µ m and only considers whether the COF in question tends to exhibit high or low values of the properties (reflected in µ m ) when predicting A mp . By comparing the imputation performance of this material bias model with the k > 0 low rank model, we quantify the extent to which the interactions between the COFs and the gas adsorption properties-encoded in m m p p terms for k > 0-are useful in the recommendation system for imputing the missing values. For each adsorption property, the stars in Fig. show Spearman's rank correlation coefficients between the value of the missing property (from the test set) and the prediction of the missing property by the benchmark material bias model. Indeed, the interaction term enhances the ability of the recommendation system to rank COFs according to their adsorption properties, though by different margins depending on the property. O 2 adsorption at (298 K, 5 bar) and N 2 adsorption at (300 K, 0.001 bar) are the two properties where the interaction term is playing only a marginal role. Overall, this indicates that our recommendation system is (i) learning interactions between COFs and the adsorption properties and (ii) more likely to suggest high-performing COFs for an application than a simpler strategy that selects COFs purely based on how they perform on average (as in the material bias model).