Machine-learning techniques to predict offsite user interactions based on onsite machine- learned models
US11004108B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 28, 2019 |
| Grant date | May 11, 2021 |
| Priority date | — |
| Expiry date | Oct 26, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for predicting an offsite entity interaction rate are provided. One approach involves using a first machine-learned model that includes a first plurality of features that correspond to entity and campaign attributes. The approach also involves training a second machine-learned model that includes a second plurality of features that includes a particular feature corresponding to predicted entity interaction rates. Thus, output of the first machine-learned model is input to the second machine-learned model. The second machine-learned model includes multiple weights that include a particular weight for the particular feature. A content request is received and a set of campaigns is identified based on an entity identifier associated with the content request. Scores are generated based on the first and second machine-learned models. Based on the scores, a campaign is selected and campaign data associated with the campaign is transmitted over a computer network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.