Systems and methods using deep joint variational autoencoders

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor cause a device to perform operations comprising:

• receiving a user identifier corresponding to a set of prior interactions;
• generating a set of candidate items;
• generating a set of latent space representations of the set of prior interactions and the candidate items using a trained inference model, wherein the trained inference model generates distribution likelihoods for hidden variables and includes a joint variational autoencoder model;
• generating a set of k-recommended items based on a comparison of the set of latent space representations of the set of prior interactions and the set of latent space representations of the candidate items;
• ranking the set of k-recommended items based on the candidate items and a set of all available items;
• and
• generating a user interface including the ranked set of k-recommended items.

A method, comprising:

• receiving a user identifier corresponding to a set of prior interactions;
• generating a set of candidate items;
• generating a set of latent space representations of the set of prior interactions and the candidate items using a trained inference model, wherein the trained inference model generates distribution likelihoods for hidden variables and includes a joint variational autoencoder model;
• generating a set of k-recommended items based on a comparison of the set of latent space representations of the set of prior interactions and the set of latent space representations of the candidate items;
• ranking the set of k-recommended items based on the candidate items and a set of all available items;
• and
• generating a user interface including the ranked set of k-recommended items.