Systems and methods using deep joint variational autoencoders
US12217296B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 31, 2022 |
| Grant date | Feb 4, 2025 |
| Priority date | — |
| Expiry date | May 15, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for generating top-k recommendation using latent space representations generated by deep joint variational autoencoder processes are disclosed. A user identifier is received and a set of prior interactions associated with the user identifier is obtained. A set of latent space representations of the set of prior interactions is generated using a trained inference model. The trained inference model includes a joint variational autoencoder model. A set of k-recommended items is generated based on a comparison of the set of latent space representations of the set of prior interactions and a set of latent space representations of one or more items. A user interface including the set of k-recommended items is generated.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.