Encoding a job posting as an embedding using a graph neural network
US11861295B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 26, 2021 |
| Grant date | Jan 2, 2024 |
| Priority date | — |
| Expiry date | Apr 27, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/1053
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Described herein are techniques for using a graph neural network to encode online job postings as embeddings. First, an input graph is defined by processing one or more rules to discover edges that connect nodes in an input graph, where the nodes of the input graph represent job postings or standardized job attributes, and the edges are determined based on analyzing a log of user activity directed to online job postings. Next, a graph neural network (GNN) is trained based on an edge prediction task. Finally, once trained, the GNN is used to derive node embeddings for the nodes (e.g., job postings) of the input graph, and in some instances, new online job postings not represented in the original input graph.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.