Machine learning for network content recommendations using embeddings representing different user instances

A system for facilitating network content recommendations for a user by generating predicted feature embeddings representing different instances of the user, the system comprising:

• one or more processors programmed with instructions that, when executed by the one or more processors, cause operations comprising:
• generating, via a machine learning model, based on prior sequential network actions of a user, a predicted set of future network actions of the user, the predicted set comprising a first subset of sequential network actions corresponding to a first predicted instance of the user and a second subset of sequential network actions corresponding to a second predicted instance of the user;
• generating a first feature embedding based on the first subset of sequential network actions and a second feature embedding based on the second subset of sequential network actions, the first feature embedding representing the first predicted instance of the user, the second feature embedding representing the second predicted instance of the user;
• querying a feature embedding space based on the first and second embeddings to obtain a third feature embedding that satisfies a similarity threshold with the first and second embeddings, wherein the similarity threshold comprises a cosine distance that is greater than a threshold cosine distance;
• and
• generating a network content recommendation for the user based on the third feature embedding that satisfies the similar threshold with the first and second embeddings.