Google llc (20240221007). SCALABLE MATRIX FACTORIZATION IN A DATABASE simplified abstract
Contents
- 1 SCALABLE MATRIX FACTORIZATION IN A DATABASE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SCALABLE MATRIX FACTORIZATION IN A DATABASE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Matrix Factorization Machine Learning Models
- 1.13 Original Abstract Submitted
SCALABLE MATRIX FACTORIZATION IN A DATABASE
Organization Name
Inventor(s)
Amir Hormati of Mountain View CA (US)
Umar Ali Syed of Edison NJ (US)
Mingge Deng of Kirkland WA (US)
SCALABLE MATRIX FACTORIZATION IN A DATABASE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240221007 titled 'SCALABLE MATRIX FACTORIZATION IN A DATABASE
Simplified Explanation
The method described in the patent application involves creating a matrix factorization machine learning model using training data, model vectors, and data vectors. The model is generated based on item vectors obtained through a linear solver, and the machine learning model is executed.
Key Features and Innovation
- Creation of a matrix factorization machine learning model based on training data.
- Determination of model vectors and data vectors.
- Calculation of dot product between model vector and data vector.
- Generation of item vectors using a linear solver.
- Execution of the matrix factorization machine learning model.
Potential Applications
This technology can be applied in various fields such as recommendation systems, personalized marketing, and content filtering.
Problems Solved
This technology addresses the need for efficient and accurate machine learning models for data analysis and prediction tasks.
Benefits
- Improved accuracy in data analysis and prediction.
- Enhanced performance of recommendation systems.
- Increased efficiency in personalized marketing strategies.
Commercial Applications
- E-commerce platforms for personalized product recommendations.
- Streaming services for content filtering and recommendation.
- Marketing companies for targeted advertising campaigns.
Prior Art
For prior art related to this technology, researchers can explore existing literature on matrix factorization machine learning models and their applications in various industries.
Frequently Updated Research
Researchers are constantly exploring new techniques and algorithms to enhance the performance of matrix factorization machine learning models. Stay updated on recent advancements in this field for improved results.
Questions about Matrix Factorization Machine Learning Models
How does the dot product calculation contribute to the accuracy of the model?
The dot product calculation helps in determining the similarity between the model vector and data vector, which is crucial for generating accurate predictions in the machine learning model.
What are the key differences between matrix factorization machine learning models and traditional regression models?
Matrix factorization models focus on decomposing the input data into latent factors, while traditional regression models rely on fitting a mathematical function to the data.
Original Abstract Submitted
a method includes obtaining a query to create a matrix factorization machine learning model based on a set of training data and determining a model vector and a data vector based on the set of training data. the method also includes determining a dot product between the model vector and the data vector, determining matrices based on the dot product, and generating item vectors using a linear solver based on the matrices. the method also includes generating the matrix factorization machine learning model based on the item vectors and executing the matrix factorization machine learning model.
