18605172. SCALABLE MATRIX FACTORIZATION IN A DATABASE simplified abstract (Google LLC)

SCALABLE MATRIX FACTORIZATION IN A DATABASE

Organization Name

Google LLC

Inventor(s)

Amir Hormati of Mountain View CA (US)

Lisa Yin of Redmond WA (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 18605172 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. This model is based on determining a model vector and a data vector from the training data, calculating a dot product between these vectors, and generating item vectors using a linear solver. The final step is generating the matrix factorization machine learning model based on these item vectors and executing it.

Key Features and Innovation

• Creation of a matrix factorization machine learning model from training data
• Determination of model vector and data vector
• Calculation of dot product between vectors
• 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 predictive analytics.

Problems Solved

This technology addresses the need for efficient and accurate machine learning models that can handle large datasets and make predictions based on complex relationships within the data.

Benefits

• Improved accuracy in predictions
• Efficient handling of large datasets
• Personalized recommendations for users
• Enhanced decision-making based on data analysis

Commercial Applications

Matrix Factorization Machine Learning Model for Personalized Recommendations This technology can be utilized by e-commerce platforms, streaming services, and online content providers to offer personalized recommendations to users, leading to increased user engagement and satisfaction.

Prior Art

Readers interested in exploring prior art related to this technology can start by researching existing matrix factorization methods in machine learning and collaborative filtering algorithms.

Frequently Updated Research

Researchers are constantly exploring new techniques and algorithms to enhance matrix factorization machine learning models for improved performance and scalability.

Questions about Matrix Factorization Machine Learning Model

How does the matrix factorization model improve recommendation systems?

The matrix factorization model enhances recommendation systems by identifying hidden patterns and relationships in the data to make accurate predictions for personalized recommendations.

What are the key components of a matrix factorization machine learning model?

The key components include model vectors, data vectors, dot product calculations, item vectors, and a linear solver to generate the final model.

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.