Systems and methods for generating representative models

A system comprising:

• one or more processors;
• a non-transitory computer-readable medium storing instructions that when executed by the one or more processors cause the one or more processors to perform operations including:
• generating a representative model using data associated with a first user and data associated with one or more second users;
• generating a training dataset using the representative model and one or more user models;
• executing a machine-learning model using the training dataset and the representative model, wherein the machine-learning model predicts a connection quality between the representative model and the one or more user models;
• establishing a communication session based on a predicted connection quality between the representative model and a particular user model;
• updating the representative model based on the data derived from the communication session;
• determining that a percentage of data of the representative model that is associated with the first user is greater than a threshold;
• removing a portion of data associated with the one or more second users from the representative model;
• updating the training dataset using the updated representative model;
• and
• retraining the machine-learning model using the updated training dataset.

A non-transitory computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to perform operations including:

• generating a representative model using data associated with a first user and data associated with one or more second users;
• generating a training dataset using the representative model and one or more user models;
• executing a machine-learning model using the training dataset and the representative model, wherein the machine-learning model predicts a connection quality between the representative model and the one or more user models;
• establishing a communication session based on a predicted connection quality between the representative model and a particular user model;
• updating the representative model based on the data derived from the communication session;
• determining that a percentage of data of the representative model that is associated with the first user is greater than a threshold;
• removing a portion of data associated with the one or more second users from the representative model;
• updating the training dataset using the updated representative model;
• and
• retraining the machine-learning model using the updated training dataset.