17948392. Data Processing Method and Apparatus simplified abstract (Huawei Technologies Co., Ltd.)
Contents
Data Processing Method and Apparatus
Organization Name
Inventor(s)
Data Processing Method and Apparatus - A simplified explanation of the abstract
This abstract first appeared for US patent application 17948392 titled 'Data Processing Method and Apparatus
Simplified Explanation
The patent application describes a data processing method in the field of artificial intelligence. Here is a simplified explanation of the abstract:
- The method involves adding an architecture parameter to each feature interaction item in a first model, resulting in a second model.
- The first model is based on a factorization machine (FM) and the architecture parameter represents the importance of a specific feature interaction item.
- Optimization is performed on the architecture parameters in the second model to obtain the optimized values.
- Based on the optimized architecture parameters and either the first or second model, a third model is obtained by deleting certain feature interaction items.
Potential Applications
This technology has potential applications in various fields, including:
- Recommendation systems: Enhancing the accuracy and efficiency of recommendation algorithms by optimizing feature interaction items.
- Natural language processing: Improving language understanding and generation models by identifying and optimizing important feature interactions.
- Fraud detection: Enhancing fraud detection systems by identifying and optimizing relevant feature interactions.
Problems Solved
The technology addresses the following problems:
- Improving model performance: By adding architecture parameters and optimizing them, the method enhances the performance of factorization machine-based models.
- Feature interaction identification: The method helps identify important feature interactions, allowing for more accurate modeling and predictions.
- Model complexity reduction: By deleting certain feature interaction items, the method simplifies the model while maintaining or improving its performance.
Benefits
The technology offers several benefits:
- Improved accuracy: By optimizing architecture parameters, the method enhances the accuracy of models in various AI applications.
- Increased efficiency: The method improves the efficiency of factorization machine-based models by identifying and focusing on important feature interactions.
- Simplified models: By deleting certain feature interaction items, the method reduces model complexity, making it easier to interpret and implement.
Original Abstract Submitted
A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.
