Abstract: Systems and methods for identifying an audience set are disclosed. A request receive identifying a future time period and item class is received and a conversion value for each of a set of user identifiers is generated by implementing a trained statistical machine learning model using historical transaction data. A first subset of user identifiers and a second subset of user identifiers are identified based on threshold values of the conversion value. The subsets are each associated with targeted advertisement types corresponding to a particular level of specificity associated with the requested item class. The first targeted advertisement type is presented to user devices associated with the first subset of user identifiers and the second targeted advertisement type is presented to user devices associated with the second subset of user identifiers.

Abstract: This application relates generally to automated systems and methods to identify an audience set for a marketing campaign period. In an embodiment, a system includes at least one processor operatively coupled with a datastore, the at least one processor configured to receive, from a user device, a request identifying a time period and an item class. The at least one processor is further configured to retrieve, from the datastore, user identifiers based on the time period and the item class. The at least one processor is further configured to determine a conversion value for each of the user identifiers by applying a statistical model to historical transaction data associated with the user identifiers. The at least one processor is further configured to determine an audience set comprising a subset of the user identifiers with the conversion value exceeding a threshold value.

Abstract: A system and method for facilitating interoperability of data transformations developed in different programming platforms under a unified platform including receiving a first transformation utilizing a first programming platform; receiving information about the first transformation; wrapping the first transformation; including the wrapped, first transformation in a transformation pipeline, the transformation pipeline including a second transformation that is wrapped, the second transformation utilizing a second programming platform different from the first programming platform; and executing the transformation pipeline including the wrapped, first transformation and the wrapped, second transformation in batch mode or real-time streaming mode.

Abstract: A system and method for predicting a user of interest's location including collecting geospatial data of a plurality of users, the plurality of users including a user of interest and other users; and generating a model for the user of interest's location based on the collected geospatial data using one or more weights associated with one or more criteria to account for data surrogacy.

Abstract: A system and method for generating monotonicity constraints and integrating the monotonicity constraints with an additive tree model includes receiving the additive tree model trained on a dataset, receiving a selection of a set of subsets of variables on which to impose monotonicity of partial dependence functions, generating a set of monotonicity constraints for the partial dependence functions in the selected set of subsets of variables based on the dataset and a set of parameters of the additive tree model, receiving a selection of an objective function, and optimizing the objective function subject to the set of monotonicity constraints.

Abstract: A system and method for using machine learning to generate a model from audited data includes a plurality of data sources, a training server having a machine learning unit, and a prediction/scoring server having a machine learning model and a data repository. The training server is coupled to receive and process information from the plurality of the resources and store it in the data repository. The training server, in particular, the machine learning unit fuses the input data and ground truth data. The machine learning unit applies machine learning to the fused input data and ground truth data to create a model. The machine learning unit then provides the model to the prediction/scoring server for use in processing new data. The prediction/scoring server uses the model to process new data and provide or take actions prescribed by the model.