Machine-learned recommender system for performance optimization of network-transferred electronic content items
US10540683B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 24, 2017 |
| Grant date | Jan 21, 2020 |
| Priority date | — |
| Expiry date | Jan 24, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Machine learning techniques are described for generating recommendations using decision trees. A decision tree is generated based on training data that comprises multiple training instances, each of which comprises a feature value for each of multiple features and a label of a target variable. The multiple features correspond to attributes of multiple content delivery campaigns. Later, feature values of a content delivery campaign are received. The decision tree is traversed using the feature values to generate output. Based on the output, one or more recommendations are identified and the one or more recommendations are presented on a computing device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.