Abstract: Systems, methods, and computer-readable storage media for combining machine learning models which respectfully use public and private data using a third machine learning model. Upon training a public data machine learning model and a private data machine learning model, the system trains a public and private data machine learning model using a combination of: (1) historical public data machine learning predictions output by the public data machine learning model, and (2) historical private data machine learning predictions output by the private data machine learning model. The system then executes the public and private data machine learning models, resulting in a public data machine learning prediction and a private data machine learning prediction, then executes the public and private data machine learning model using the public data machine learning prediction and the private data machine learning prediction as inputs, resulting in a final prediction.

Abstract: Systems, methods, and computer-readable storage media for aggregating the outputs of multiple machine learning models, then using the output of yet another machine learning model as a multiplier to obtain a final prediction. A system can receiving a plurality of data sets, each data set being associated with at least one data type, and train machine learning models, each model associated with one or more of the different data types. Upon execution, the multiple machine learning models can each produce a prediction which is aggregated together to form an aggregated prediction. The multiplier from the additional machine learning model can then be applied to the aggregated prediction, resulting in a final prediction.

Abstract: Systems, methods, and computer-readable storage media for recommending loads for transport. A system can receive location coordinates for a transport vehicle, and further receive data regarding available loads which can be transported by the transport vehicle. The system can then filter the available loads based at least in part on the location coordinates. The system can also receive at least one carrier profile and at least one shipper profile. Finally, the system can execute a load recommendation algorithm using the preference filtered loads, the at least one carrier profile, and the at least one shipper profile as inputs, resulting in at least one load recommendation score for a load within the preference filtered loads.