Systems and methods for matching data entities

A computer-implemented method, comprising:

• receiving, from a requesting system, a determination request;
• for each of a first data entity and a second data entity, generating
• generating, using a trained classification model, a similarity score between the first data entity and the second data entity that accounts for feature interactions between the first data entity and the second data entity, wherein the trained classification model receives the demarcated attributes for each of the first data entity and the second data entity as inputs, generates a combined vector based on combining the first data entity and the second data entity, generates a product vector based on the first data entity and the second data entity, generates a difference vector based on the first data entity and the second data entity, concatenates the product vector and the difference vector to generate a first concatenated vector representing a bi-linear interaction between features of the first data entity and the second data entity, concatenates the combined vector and the first concatenated vector to generate a second concatenated vector, and accounts for the feature interactions based on the second concatenated vector, and wherein the trained classification model is trained using a dataset of annotated pairs of data entities and wherein weights in the trained classification model are determined based on a customizable loss function;
• and
• in accordance with a determination that the similarity score is greater than a predetermined threshold, generating an indication that the first data entity matches the second data entity.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by a processor, cause a device to perform operations comprising:

• receiving, from a requesting system, a determination request;
• for each of a first data entity and a second data entity, generating:
• a sequence of demarcated attributes;
• generating, using a trained classification model, a similarity score between the first data entity and the second data entity that accounts for feature interactions between the first data entity and the second data entity, wherein the trained classification model receives the demarcated attributes for each of the first data entity and the second data entity as inputs, generates a combined vector based on combining the first data entity and the second data entity, generates a product vector based on the first data entity and the second data entity, generates a difference vector based on the first data entity and the second data entity, concatenates the product vector and the difference vector to generate a first concatenated vector representing a bi-linear interaction between features of the first data entity and the second data entity, concatenates the combined vector and the first concatenated vector to generate a second concatenated vector, and accounts for the feature interactions based on the second concatenated vector, and wherein the trained classification model is trained using a dataset of annotated pairs of data entities, and wherein weights in the trained classification model are determined based on a customizable loss function;
• and
• in accordance with a determination that the similarity score is greater than a predetermined threshold, generating an indication that the first data entity matches the second data entity.