Personalization platform for user-specific recommendations using artificial intelligence model selections from user privacy controls

A system comprising:

• a non-transitory memory;
• and
• one or more hardware processors coupled to the non-transitory memory and configured to execute instructions to cause the system to:
• detect a session between a computing device of a user and a digital shopping platform associated with a merchant during a digital interaction between the user and the merchant;
• store first data associated with a first set of machine learning (ML) features for a plurality of ML models utilizable for a personalization of recommendations during digital interactions;
• query a privacy management platform for an authorization to access a profile of the user based on a privacy control of the user for the profile;
• authorize an access to the profile based on a response from the privacy management platform;
• determine second data associated with a second set of ML features of one or more of the plurality of ML models based on data from the profile, wherein the second set of ML features comprise additional ML features usable by the one or more of the plurality of ML models for the personalization of the recommendations;
• select a first ML model from the one or more of the plurality of ML models that utilize the second set of ML features based on at least one of the profile data or the digital interaction;
• and
• generate a recommendation for the user using the first ML model and based on the first and second data for the first and second sets of ML features.

A non-transitory machine-readable medium having instructions stored thereon, the instructions executable to cause performance of operations comprising:

• detecting a session between a computing device of a user and a digital shopping platform associated with a merchant, wherein the session is associated with a shopping session of the user with the merchant;
• determining an authorization for accessing a profile of the user based on a privacy control of the user and a privacy management platform that manages access to the profile;
• accessing profile data for the user based on the authorization;
• selecting a first machine learning (ML) model configured for first product recommendations from a plurality of ML models based on the profile data and ML features of the plurality of ML models;
• generating a recommendation for the user using the first ML model and based on ML feature data associated with at least one of the profile data or the shopping session;
• detecting a behavior event of the user that is associated with at least one of the recommendation or the shopping session;
• determining additional ML feature data associated with the shopping session based on processing the behavior event;
• and
• storing the additional ML feature data that enables one or more additional recommendations to be determined by the first ML model for the user