Recommender system using bayesian graph convolution networks
US11494617B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 12, 2020 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | May 2, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0631
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
System and method for processing an observed bipartite graph that has a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes such that at least some nodes have node neighbourhoods comprising edge connections to one or more other nodes. A plurality of random graph topologies are derived that are realizations of the observed graph topology by replacing the node neighbourhoods of at least some nodes with the node neighbourhoods of other nodes. A non-linear function is trained using the plurality of user nodes, plurality of item nodes and plurality of random graph topologies to learn user node embeddings and item node embeddings for the plurality of user nodes and plurality of item nodes, respectively.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.