Google llc (20240242106). MACHINE LEARNING RANK AND PREDICTION CALIBRATION simplified abstract
Contents
MACHINE LEARNING RANK AND PREDICTION CALIBRATION
Organization Name
Inventor(s)
Gil Shamir of Sewickley PA (US)
Zhuoshu Li of Pittsburgh PA (US)
MACHINE LEARNING RANK AND PREDICTION CALIBRATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242106 titled 'MACHINE LEARNING RANK AND PREDICTION CALIBRATION
Simplified Explanation: The patent application describes methods, systems, and apparatus for training and using machine learning models to predict engagement and rank digital components based on feature values.
Key Features and Innovation:
- Utilizes two machine learning models to predict engagement and rank digital components.
- Selects a subset of digital components based on likelihood scores from the first model.
- Trains the second model to output engagement predictions and rankings for the selected digital components.
Potential Applications: This technology can be applied in digital marketing, e-commerce platforms, recommendation systems, and personalized content delivery.
Problems Solved: Addresses the challenge of efficiently predicting engagement and ranking digital components to enhance user experience and increase conversion rates.
Benefits:
- Improves user engagement by providing personalized recommendations.
- Enhances the efficiency of content delivery and marketing strategies.
- Increases conversion rates by targeting high-ranking digital components.
Commercial Applications: Potential commercial applications include digital advertising platforms, online retail websites, social media platforms, and content streaming services.
Prior Art: Readers can explore prior research on machine learning models for engagement prediction and content ranking in digital platforms.
Frequently Updated Research: Stay informed about the latest advancements in machine learning algorithms for personalized recommendations and content ranking.
Questions about Machine Learning Models for Engagement Prediction: 1. How do machine learning models improve user engagement in digital platforms? 2. What are the key factors considered in training machine learning models for engagement prediction?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium for training and using machine learning (ml) models. in one aspect, a method includes receiving a digital component request. a first ml model can output scores indicating a likelihood of a positive outcome for digital components. input data can be provided to a second ml model and can include feature values for a subset of digital components that were selected based on the output scores. the second ml model can be trained to output an engagement predictions and/or ranking of digital components based at least in part on feature values of digital components that will be provided together as recommendations, and can produce a second output that includes ranking and engagement predictions of the digital components in the subset of digital components. at least one digital component can be provided based on the second output.
