Controlling item frequency using a machine-learned model
US11093861B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 20, 2019 |
| Grant date | Aug 17, 2021 |
| Priority date | — |
| Expiry date | Jan 29, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0251
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for controlling item frequency using machine learning are provides. In one technique, two prediction models are trained: one based on interaction history of multiple content items by multiple entities and the other based on predicted interaction rates and an impression count for each of multiple content items. In response to a request, a particular entity associated with the request is identified and multiple candidate content items are identified. For each identified candidate content item, the first prediction model is used to determine a predicted interaction rate, an impression count of the candidate content item is determined with respect to the particular entity, the second prediction model is used to generate an adjustment based on the impression count, and an adjusted entity interaction rate is generated based on the predicted interaction rate and the adjustment. A particular candidate content item is selected based on the generated adjusted entity interaction rates.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.