Systems and methods for search machine learning

A system, comprising:

• at least one processor;
• and memory storing instructions that, when executed by the at least one processor, cause the system to perform operations including:
• generating, based on a machine learning model, a predicted feature of a user by inputting a representation of a user profile of the user into the machine learning model, wherein the machine learning model is generated by:
• receiving user data from a database;
• generating a first training set by extracting multi-dimensional feature representations from historical online engagement activities of a plurality of sample users;
• training a machine learning model in a first stage using the first training set to generate initial classification boundaries for user interest prediction;
• automatically detecting classification errors by performing at least one of:
• identifying, from the plurality of sample users, a first subset of users incorrectly classified as interested based on digital engagement metrics;
• or
• identifying, from the plurality of sample users, a second subset of sample users incorrectly classified as uninterested based on digital engagement metrics;
• constructing a second training dataset based on the first training set with the identified classification errors;
• and
• retraining the machine learning model using the second training dataset;
• and
• executing a real-time digital resource allocation operation based on the predicted feature of the user.

A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations including:

• generating, based on a machine learning model, a predicted feature of a user by inputting a representation of a user profile of the user into the machine learning model, wherein the machine learning model is generated by:
• receiving user data of a plurality of sample users from a database;
• generating a first training set by extracting multi-dimensional feature representations from historical online engagement activities of the plurality of sample users;
• training a machine learning model in a first stage using the first training set to generate initial classification boundaries for user interest prediction;
• automatically detecting classification errors by performing at least one of:
• identifying, from the plurality of sample users, a first subset of users incorrectly classified as interested based on digital engagement metrics;
• or
• identifying, from the plurality of sample users, a second subset of sample users incorrectly classified as uninterested based on digital engagement metrics;
• constructing a second training dataset based on the first training set with the identified classification errors;
• and
• retraining the machine learning model using the second training dataset;
• and
• executing a real-time digital resource allocation operation based on the predicted feature of the user.