Abstract: Systems and methods for determining items in a target domain to recommend to a user whom has not previously interacted with items in the target domain is described. The method comprises generating an auxiliary domain user embedding based on user affinities for each of a plurality of items in an auxiliary domain and embeddings for each of the plurality of items in the auxiliary domain, providing the auxiliary domain user embedding as input to a neural network configured to output a target domain user embedding, predicting target domain user affinities for items in the target domain based, at least in part, on a similarity measure between the target domain user embedding and an embedding for at least one item in the target domain, and determining a set of items in the target domain to recommend to the user based, at least in part, on the predicted target domain user affinities.

Abstract: Methods and systems for generating entity embeddings for use with one or more machine learning models are described. The system comprises at least one storage device configured to implement a feature registry for storing features associated with at least one entity and at least one computer processor. The at least one computer processor is programmed to generate at least one entity embedding for the at least one entity, perform a plurality of benchmarking tasks on the generated at least one entity embedding to generate benchmarking data, and publish the at least one entity embedding and the benchmarking data to the feature registry to enable the at least one entity embedding to be shared among a plurality of machine learning models.

Abstract: Systems and methods for determining items in a target domain to recommend to a user whom has not previously interacted with items in the target domain is described. The method comprises generating an auxiliary domain user embedding based on user affinities for each of a plurality of items in an auxiliary domain and embeddings for each of the plurality of items in the auxiliary domain, providing the auxiliary domain user embedding as input to a neural network configured to output a target domain user embedding, predicting target domain user affinities for items in the target domain based, at least in part, on a similarity measure between the target domain user embedding and an embedding for at least one item in the target domain, and determining a set of items in the target domain to recommend to the user based, at least in part, on the predicted target domain user affinities.