Two-tower neural network for content-audience relationship prediction

A method comprising:

• passing data about a first user to a first generator model to produce a user semantic embedding, the user semantic embedding being a first point in a latent n-dimensional space, the first point representing a semantic meaning of the data about the first user;
• passing the data about a first piece of content to the first generator model to produce a content semantic embedding, the content semantic embedding being a second point in the latent n-dimensional space, the second point representing a semantic meaning of the data about the first piece of content;
• and
• feeding the user semantic embedding and the content semantic embedding to a two-tower deep neural network trained to predict a probability of a first type of engagement between the first user and the first piece of content, the two-tower deep neural network fine-tuning the user semantic embedding into a user embedding and the content semantic embedding into a content embedding, each tower of the two-tower deep neural network trained using shared engagement data, the probability determined based on a geometric relationship between the user embedding and the content embedding.

A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:

• passing data about a first user to a first generator model) to produce a user semantic embedding, the user semantic embedding being a first point in a latent n-dimensional space, the first point representing a semantic meaning of the data about the first user;
• passing the data about a first piece of content to the first generator model to produce a content semantic embedding, the content semantic embedding being a second point in the latent n-dimensional space, the second point representing a semantic meaning of the data about the first piece of content;
• and
• feeding the user semantic embedding and the content semantic embedding to a two-tower deep neural network trained to predict a probability of a first type of engagement between the first user and the first piece of content, the two-tower deep neural network fine-tuning the user semantic embedding into a user embedding and the content semantic embedding into a content embedding, each tower of the two-tower deep neural network trained using shared engagement data, the probability determined based on a geometric relationship between the user embedding and the content embedding.