Few-shot learning for multi-task recommendation systems
US12236345B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 17, 2021 |
| Grant date | Feb 25, 2025 |
| Priority date | — |
| Expiry date | Dec 28, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0985
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.