Abstract: Described are a system, method, and computer program product for debiasing embedding vectors of machine learning models. The method includes receiving embedding vectors and generating two clusters thereof. The method includes determining a first mean vector of the first cluster and a second mean vector of the second cluster. The method includes determining a bias associated with each of a plurality of first candidate vectors and replacing the first mean vector with a first candidate vector based on the bias. The method includes determining a bias associated with each of a plurality of second candidate vectors and replacing the second mean vector with a second candidate vector based on the bias. The method includes repeatedly replacing the first and second mean vectors until an extremum of the bias score is reached, and debiasing the embedding vectors by linear projection using a direction defined by the first and second mean vectors.

Abstract: A computer-implemented method for providing merchant recommendations comprises receiving, by a processor, raw merchant embeddings generated from payment transaction records, wherein the raw merchant embeddings include a plurality of embedded features entangled in an embedding space. The processor uses transaction metadata associated with the payment transaction records to determine a subspace of an identified feature within the embedding space. A linear transformation process then removes the subspace of the identified feature from the embedding space to create modified merchant embeddings that are merged and aligned with other ones of the plurality of features within the embedding space. The processor automatically generates a list of merchant rankings based on the modified merchant embeddings, past preferences of a target user using raw user embeddings, and a target region, and provides the list of merchant rankings to the target user.