Microsoft technology licensing, llc (20240134867). ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING simplified abstract
Contents
- 1 ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING - 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
ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING
Organization Name
microsoft technology licensing, llc
Inventor(s)
Liyan Fang of Sunnyvale CA (US)
Andrew O. Hatch of Berkeley CA (US)
Keqing Liang of Cupertino CA (US)
Yafei Wei 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 20240134867 titled 'ENTITY SELECTION AND RANKING USING DISTRIBUTION SAMPLING
Simplified Explanation
The disclosed technologies involve generating a reward score for an entity, determining a rate distribution based on the reward score and the number of times the entity has been selected for ranking, generating a sampled rate value, creating a probability score for a pair of the entity and a user, determining a probability distribution using the probability score, generating a sampled probability value, and training a machine learning model using the sampled probability value.
- Reward score generated for an entity
- Rate distribution determined based on reward score and number of times entity selected for ranking
- Sampled rate value generated by sampling rate distribution
- Probability score generated for entity-user pair
- Probability distribution determined using probability score
- Sampled probability value generated by sampling probability distribution
- Machine learning model trained using sampled probability value
Potential Applications
This technology could be applied in recommendation systems, personalized marketing, and targeted advertising.
Problems Solved
This technology helps in improving user engagement, increasing conversion rates, and enhancing user experience by providing more relevant and personalized recommendations.
Benefits
The benefits of this technology include increased user satisfaction, higher click-through rates, improved customer retention, and more effective marketing campaigns.
Potential Commercial Applications
- Personalized recommendation engines
- Targeted advertising platforms
- E-commerce product recommendation systems
Possible Prior Art
One possible prior art could be collaborative filtering algorithms used in recommendation systems.
What is the impact of this technology on user privacy?
This technology may raise concerns about user privacy as it involves collecting and analyzing user data to generate personalized recommendations.
How does this technology handle scalability issues?
This technology may face scalability challenges when dealing with a large number of entities and users. Implementing efficient algorithms and infrastructure can help address scalability issues.
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 and a number of times the entity has been selected for ranking. 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 based on 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.
