Method, System, and Computer Program Product for Improving Machine Learning Models

A system comprising at least one processor configured to:

• receive a data set comprising a plurality of data records, each data record of the plurality of data records associated with an entity of a plurality of entities;
• input the data set to a pre-trained first machine learning model to generate a plurality of first embedding vectors, each respective first embedding vector of the plurality of first embedding vectors associated with a respective entity of the plurality of entities;
• input the plurality of first embedding vectors to a second machine learning model to generate a plurality of second embedding vectors by mapping each respective first embedding vector of the plurality of first embedding vectors to a respective second embedding vector in a user-specific embedding space associated with a user;
• input the plurality of second embedding vectors to a third machine learning model to extract feature data associated with at least one feature based on the plurality of second embedding vectors;
• input an output from a machine learning system and the feature data to a fourth machine learning model to generate a relevance score for each entity of the plurality of entities;
• determine a subset of entities of the plurality of entities based on the relevance score for each entity of the subset of entities;
• communicate a feedback request to a user device of the user based on the subset of entities;
• receive feedback data from the user device for each entity of the subset of entities;
• and
• train at least one of the following:
• the second machine learning model, the third machine learning model, the fourth machine learning model, or any combination thereof based on the feedback data.

A computer program product, comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:

• receive a data set comprising a plurality of data records, each data record of the plurality of data records associated with an entity of a plurality of entities;
• input the data set to a pre-trained first machine learning model to generate a plurality of first embedding vectors, each respective first embedding vector of the plurality of first embedding vectors associated with a respective entity of the plurality of entities;
• input the plurality of first embedding vectors to a second machine learning model to generate a plurality of second embedding vectors by mapping each respective first embedding vector of the plurality of first embedding vectors to a respective second embedding vector in a user-specific embedding space associated with a user;
• input the plurality of second embedding vectors to a third machine learning model to extract feature data associated with at least one feature based on the plurality of second embedding vectors;
• input an output from a machine learning system and the feature data to a fourth machine learning model to generate a relevance score for each entity of the plurality of entities;
• determine a subset of entities of the plurality of entities based on the relevance score for each entity of the subset of entities;
• communicate a feedback request to a user device of the user based on the subset of entities;
• receive feedback data from the user device for each entity of the subset of entities;
• and
• train at least one of the following:
• the second machine learning model, the third machine learning model, the fourth machine learning model, or any combination thereof based on the feedback data.