Digital content interaction prediction and training that addresses imbalanced classes
US11676060B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 20, 2016 |
| Grant date | Jun 13, 2023 |
| Priority date | — |
| Expiry date | Nov 13, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Digital content interaction prediction and training techniques that address imbalanced classes are described. In one or more implementations, a digital medium environment is described to predict user interaction with digital content that addresses an imbalance of numbers included in first and second classes in training data used to train a model using machine learning. The training data is received that describes the first class and the second class. A model is trained using machine learning. The training includes sampling the training data to include at least one subset of the training data from the first class and at least one subset of the training data from the second class. Iterative selections are made of a batch from the sampled training data. The iteratively selected batches are iteratively processed by a classifier implemented using machine learning to train the model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.