Engaging unknowns in response to interactions with knowns

A system for improving distributed network data flow efficiency by using a machine learning model to engage a reduced set of unknown objects, the system comprising:

• a. at least one processor;
• b. a communication interface communicatively coupled to the at least one processor;
• and
• c. a memory device storing executable code, that when executed, causes the at least one processor to:
• i. train, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects to reciprocate, the training including:
• a. iteratively predicting which of the unknown objects based on a profile for each unknown object, will reciprocate, which of the unknown objects will reciprocate the predicting being based on at least one output category;
• b. test and compare the unknown objects predicting during each iteration against a target variable;
• and
• c. indicate, via feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects improve predictability of the target variable,
• ii. deploy the trained machine learning model to generate a reduced set of unknown objects to reciprocate, and based thereon determining a reduced set of unknown objects to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object;
• and
• iii. trigger a communication, based on the profiles of each unknown object, to each unknown object of the reduced set of unknown objects,
• d. record, in an interaction map, which of the unknown objects have been sent a communication;
• e. receive, from an unknown object, a reciprocating communication;
• and
• f. record, in the interaction map, that the unknown object has reciprocated the communication.

A method for improving distributed network data flow efficiency by using a machine learning model to engage a reduced set of unknown objects, the method comprising:

• a. training, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects to reciprocate, the training including:
• i. iteratively predicting which of the unknown objects, based on a profile for each unknown object, which of the unknown objects are will reciprocate, the predicting being based on at least one output category;
• ii. testing and comparing the unknown objects predicted during each iteration against a target variable;
• and
• iii. indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects improve predictability of the target variable;
• b. deploying the trained machine learning model to generate a reduced set of unknown objects to reciprocate, and based thereon determining a reduced set of unknown objects to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object;
• and
• c. triggering a communication, based on the profile of each unknown object, to each unknown object of the reduced set of unknown objects.