Training a machine learned model to determine relevance of items to a query using different sets of training data from a common domain
US12222937B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 9, 2022 |
| Grant date | Feb 11, 2025 |
| Priority date | — |
| Expiry date | Jun 16, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0633
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.