Compact entity identifier embeddings

A system comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the system to:
• using a first set of hash values as indexes, retrieve, from a first set of lookup tables, a first set of intermediate vectors that represent a first entity identifier (ID), wherein the first set of intermediate vectors has a dimensionality comprising a common number of dimensions associated with the first entity and a dimension comprises a numeric value;
• aggregate corresponding dimensions of the first set of intermediate vectors using an element-wise aggregation to produce a first embedding that represents the first entity ID and has the same dimensionality as the first set of intermediate vectors;
• apply a machine learning model to an embedding pair comprising the first embedding and a second embedding that represents a second entity ID, wherein the second embedding is produced by element-wise aggregation of a second set of intermediate vectors retrieved from a second set of lookup tables using a second set of hash values as indexes and has the same dimensionality as the first embedding;
• based on the embedding pair, generate, by the machine learning model, output that represents a likelihood value, wherein the likelihood value is associated with a positive label as an indication of a positive interaction between the first entity and a second entity associated with the second entity ID;
• and
• train the machine learning model to predict an outcome associated with the first entity and the second entity based on the first embedding and an additional input.

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform operations comprising:

• applying, by one or more computer systems, a first set of hash functions to a first entity identifier (ID) for a first entity to generate a first set of hash values;
• using the first set of hash values as indexes, retrieving, from a first set of lookup tables, a first set of intermediate vectors that represent the first entity ID, wherein the first set of intermediate vectors has a dimensionality comprising a common number of dimensions associated with the first entity and a dimension comprises a numeric value;
• aggregating corresponding dimensions of the first set of intermediate vectors using an element-wise aggregation to produce a first embedding that represents the first entity ID and has the same dimensionality as the first set of intermediate vectors;
• applying a machine learning model to an embedding pair comprising the first embedding and a second embedding that represents a second entity ID, wherein the second embedding is produced by element-wise aggregation of a second set of intermediate vectors retrieved from a second set of lookup tables using a second set of hash values as indexes and has the same dimensionality as the first embedding;
• based on the embedding pair, generating, by the machine learning model, output that represents a likelihood value, wherein the likelihood value is associated with a positive label as an indication of a positive interaction between the first entity and a second entity associated with the second entity ID;
• and
• training the machine learning model to predict an outcome associated with the first entity and the second entity based on the first embedding and an additional input.