Learning a ranking model using interactions of a user with a jobs list
US9626654B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 30, 2015 |
| Grant date | Apr 18, 2017 |
| Priority date | — |
| Expiry date | Jun 30, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q50/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.