Microsoft technology licensing, llc (20240119278). TRANSFER LEARNING FOR SENIORITY MODELING LABEL SHORTAGE simplified abstract
Contents
- 1 TRANSFER LEARNING FOR SENIORITY MODELING LABEL SHORTAGE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRANSFER LEARNING FOR SENIORITY MODELING LABEL SHORTAGE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
TRANSFER LEARNING FOR SENIORITY MODELING LABEL SHORTAGE
Organization Name
microsoft technology licensing, llc
Inventor(s)
Zheng Zhang of San Carlos CA (US)
Jacob Bollinger of San Francisco CA (US)
TRANSFER LEARNING FOR SENIORITY MODELING LABEL SHORTAGE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119278 titled 'TRANSFER LEARNING FOR SENIORITY MODELING LABEL SHORTAGE
Simplified Explanation
The patent application describes techniques for using transfer learning to address label data shortage in seniority modeling for an online service.
- Training an initialized neural network using training examples comprising profile data and labels for the profile data, where each label comprises a standardized position title.
- Training the pre-trained neural network using training examples comprising profile data and labels for the profile data, where the labels comprise a position seniority.
- Computing the position seniority for a user based on profile data of the user using the fine-tuned neural network.
- Using the position seniority of the user in an application of an online service.
Potential Applications
The technology can be applied in human resources for determining seniority levels of employees, in online platforms for personalized user experiences based on seniority, and in talent management systems for identifying high-potential candidates.
Problems Solved
This technology addresses the issue of label data shortage in seniority modeling, improves accuracy in determining position seniority, and enhances the efficiency of online services by providing personalized experiences based on user seniority.
Benefits
The benefits of this technology include improved accuracy in determining position seniority, enhanced user experience in online services, and increased efficiency in talent management processes.
Potential Commercial Applications
The technology can be commercially applied in HR software solutions, online platforms for career development, and talent acquisition tools to streamline the process of identifying and managing seniority levels.
Possible Prior Art
One possible prior art could be the use of transfer learning in machine learning models for personalized recommendations in e-commerce platforms. Another could be the application of neural networks in natural language processing for sentiment analysis in social media platforms.
What are the potential limitations of this technology?
The potential limitations of this technology could include the need for a large amount of labeled data for training the neural network effectively, the challenge of interpreting the results accurately, and the requirement for continuous updates to adapt to changing user profiles and job titles.
How does this technology compare to existing solutions in the market?
This technology offers a more efficient and accurate way of determining position seniority based on profile data compared to traditional methods that rely solely on manual input or limited data sources. It provides a more personalized and dynamic approach to seniority modeling in online services.
Original Abstract Submitted
techniques for using transfer learning to address label data shortage in seniority modeling for an online service are disclosed herein. in some embodiments, a computer-implemented method comprises training an initialized neural network using training examples comprising profile data and labels for the profile data, where each label comprises a standardized position title, and the training of the initialized neural network forms a pre-trained neural network. next, the computer system may train the pre-trained neural network using training examples comprising profile data and labels for the profile data, where the labels comprise a position seniority, and the training of the pre-trained neural network forms a fine-tuned neural network. the computer system may then compute the position seniority for a user based on profile data of the user using the fine-tuned neural network, and use the position seniority of the user in an application of an online service.
