Abstract: A method for audience recommendation using node similarity in combined contextual graph embeddings can include receiving a merchant identifier of a merchant and generating one or more merchant tags describing merchant data corresponding to the merchant. A set of audience embeddings can be generated from a set of audience auxiliary data using an audience taxonomy and a set of merchant embeddings can be generated from the merchant data relating to the merchant using the one or more merchant tags. The set of audience embeddings and the set of merchant embeddings are used to produce a heterogenous information network of combined audience data and merchant data, which is then analyzed to identify relationships between each audience and the merchant. A score for one or more audiences can be determined based on the relationships between the one or more audiences and the merchant.

Abstract: A method for determining the validity of a computational model using a blockchain and zero knowledge principles includes: storing, in a memory of a first computing system, a computational model; receiving, by a receiver of the first computing system, a blockchain data value from one block of a plurality of blocks comprising a blockchain, wherein the blockchain data value includes a data set; receiving, by the receiver of the first computing system, an expected accuracy value; applying, by a processor of the first computing system, the data set to the computational model to generate a result value; and determining, by the processor of the first computing system, a validity measurement for the computational model based on a comparison of the generated result value and the expected accuracy value.

Abstract: A method for audience recommendation using node similarity in combined contextual graph embeddings can include receiving a merchant identifier of a merchant and generating one or more merchant tags describing merchant data corresponding to the merchant. A set of audience embeddings can be generated from a set of audience auxiliary data using an audience taxonomy and a set of merchant embeddings can be generated from the merchant data relating to the merchant using the one or more merchant tags. The set of audience embeddings and the set of merchant embeddings are used to produce a heterogenous information network of combined audience data and merchant data, which is then analyzed to identify relationships between each audience and the merchant. A score for one or more audiences can be determined based on the relationships between the one or more audiences and the merchant.