18009178. TRANSFER MACHINE LEARNING FOR ATTRIBUTE PREDICTION simplified abstract (GOOGLE LLC)
TRANSFER MACHINE LEARNING FOR ATTRIBUTE PREDICTION
Organization Name
Inventor(s)
Alexander E. Mayorov of Kirkland WA (US)
TRANSFER MACHINE LEARNING FOR ATTRIBUTE PREDICTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18009178 titled 'TRANSFER MACHINE LEARNING FOR ATTRIBUTE PREDICTION
Simplified Explanation
The abstract describes methods, systems, and apparatus for using transfer machine learning to predict attributes based on contextual information provided by a user.
- Receiving a digital component request from a user's device with contextual information for a display environment.
- Converting the contextual information into input data for a transfer machine learning model.
- Training the model using training data from subscriber users to predict user attributes of non-subscribing users.
- Adapting the model to make predictions for users viewing electronic resources to which they are not subscribed.
Potential Applications
- Personalized content recommendation systems
- Targeted advertising based on user attributes
- User behavior prediction for website optimization
Problems Solved
- Predicting user attributes without direct user input
- Improving user experience by tailoring content to individual preferences
- Enhancing marketing strategies by targeting specific user segments
Benefits
- Increased user engagement with personalized content
- More effective advertising campaigns
- Improved user satisfaction with tailored experiences.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using transfer machine learning to predict attributes are described. In one aspect, a method includes receiving, from a client device of a user, a digital component request that includes at least input contextual information for a display environment in which a selected digital component will be displayed. The contextual information is converted into input data that includes input feature values for a transfer machine learning model trained to output predictions of user attributes of users based on feature values for features representing display environments. The transfer machine learning model is trained using training data for subscriber users obtained from a data pipeline associated with electronic resources to which the subscriber users are subscribed and adapted to predict user attributes of non-subscribing users viewing electronic resources to which the non-subscribing users are not subscribed.
