18048410. ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING simplified abstract (Microsoft Technology Licensing, LLC)

ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Yafei Wei of Sunnyvale CA (US)

Andrew O. Hatch of Berkeley CA (US)

Keqing Liang of Cupertino CA (US)

Liyan Fang of Sunnyvale CA (US)

Ankan Saha of San Francisco CA (US)

ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18048410 titled 'ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING

Simplified Explanation

The abstract of the patent application describes a technology that involves generating a reward score for an entity, determining a rate distribution using the reward score, generating a sampled rate value, generating a probability score for a pair of the entity and a user using the sampled rate value, determining a probability distribution using the probability score, generating a sampled probability value, and training a machine learning model using the sampled probability value.

• Generating reward scores for entities
• Determining rate distributions based on reward scores
• Generating sampled rate values by sampling rate distributions
• Generating probability scores for entity-user pairs
• Determining probability distributions based on probability scores
• Generating sampled probability values by sampling probability distributions
• Training machine learning models using sampled probability values

Potential Applications

The technology can be applied in various fields such as:

• Personalized recommendation systems
• Fraud detection algorithms
• Targeted advertising platforms

Problems Solved

This technology addresses challenges such as:

• Improving accuracy in predicting user behavior
• Enhancing personalized user experiences
• Optimizing resource allocation based on user preferences

Benefits

The benefits of this technology include:

• Increased efficiency in decision-making processes
• Enhanced user engagement and satisfaction
• Improved overall performance of machine learning models

Potential Commercial Applications

The technology can be utilized in industries like:

• E-commerce for personalized product recommendations
• Financial services for fraud detection and risk assessment
• Marketing for targeted advertising campaigns

Possible Prior Art

One possible prior art could be the use of machine learning models for personalized recommendations in e-commerce platforms. Another could be the application of probability distributions in fraud detection algorithms.

Unanswered Questions

How does this technology handle large datasets efficiently?

The article does not provide information on the scalability of the technology and its performance with big data.

What are the potential limitations of this technology in real-world applications?

The article does not discuss any potential drawbacks or challenges that may arise when implementing this technology in practical scenarios.

Original Abstract Submitted

Embodiments of the disclosed technologies include generating a reward score for an entity. A rate distribution is determined using the reward score. A sampled rate value is generated by sampling the rate distribution. A probability score is generated for a pair of the entity and a user using the sampled rate value. A probability distribution is determined using the probability score. A sampled probability value is generated by sampling the probability distribution. A machine learning model is trained using the sampled probability value.