Personalized hyperparameter tuning with contextual multi-arm bandit and reinforcement learning

A system for improving performance of a machine learning (ML) based algorithm used to provide a user experience to a user via a media device, comprising:

• one or more memories;
• and
• at least one processor each coupled to at least one of the memories and configured to perform operations comprising:
• selecting a first set of hyperparameter values;
• implementing a first iteration of the ML based algorithm based on the first set of hyperparameter values;
• utilizing the first iteration of the ML based algorithm to provide a first user experience to the user via the media device;
• determining a response of the user to the first user experience;
• selecting, by a hyperparameter tuning ML model and based at least on the response of the user, a second set of hyperparameter values, wherein the hyperparameter tuning ML model comprises one of a contextual multi-arm bandit (CMAB) model or a reinforcement learning (RL) model;
• implementing a second iteration of the ML based algorithm based on the second set of hyperparameter values;
• and
• utilizing the second iteration of the ML based algorithm to provide a second user experience to the user via the media device.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations for improving performance of a machine learning (ML) based algorithm used to provide a user experience to a user via a media device, the operations comprising:

• selecting a first set of hyperparameter values;
• implementing a first iteration of the ML based algorithm based on the first set of hyperparameter values;
• utilizing the first iteration of the ML based algorithm to provide a first user experience to the user via the media device;
• determining a response of the user to the first user experience;
• selecting, by a hyperparameter tuning ML model and based at least on the response of the user, a second set of hyperparameter values, wherein the hyperparameter tuning ML model comprises one of a contextual multi-arm bandit (CMAB) model or a reinforcement learning (RL) model;
• implementing a second iteration of the ML based algorithm based on the second set of hyperparameter values;
• and
• utilizing the second iteration of the ML based algorithm to provide a second user experience to the user via the media device.