Abstract: Machine-learned model scoring techniques for reducing model invocations are provided. In one technique, first values for non-pair-specific interaction features that are associated with a first entity are identified. Second values for pair-specific interaction features that are associated with (a) the first entity and (b) a second entity that is different than the first entity are identified. A machine-learned model that has been trained based on the non-pair-specific interaction features and the pair-specific interaction features generates a first score based on the first values. The machine-learned model also generates a second score based on the first values and the second values. A final score for the first entity-second entity pair is computed based on the first score and the second score. Based on the final score, data about the first entity is transmitted over a computer network to a computing device associated with the second entity.