Google llc (20240103893). GENERATING CONTENT ENDORSEMENTS USING MACHINE LEARNING NOMINATOR(S) simplified abstract
Contents
- 1 GENERATING CONTENT ENDORSEMENTS USING MACHINE LEARNING NOMINATOR(S)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING CONTENT ENDORSEMENTS USING MACHINE LEARNING NOMINATOR(S) - 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
GENERATING CONTENT ENDORSEMENTS USING MACHINE LEARNING NOMINATOR(S)
Organization Name
Inventor(s)
Deepak Ramachandran of Sunnyvale CA (US)
Sarvjeet Singh of Palo Aito CA (US)
Tania Bedrax-weiss of Sunnyvale CA (US)
GENERATING CONTENT ENDORSEMENTS USING MACHINE LEARNING NOMINATOR(S) - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240103893 titled 'GENERATING CONTENT ENDORSEMENTS USING MACHINE LEARNING NOMINATOR(S)
Simplified Explanation
The patent application describes techniques for generating candidate endorsements for recommended items of content using an ensemble of nominators. Each nominator in the ensemble provides a candidate endorsement for each recommended item, and an endorsement is selected based on a score determined for each candidate endorsement.
- Techniques for generating candidate endorsements for recommended content items
- Ensemble of nominators providing candidate endorsements
- Selection of endorsements based on scores
Potential Applications
The technology could be applied in recommendation systems for various platforms such as e-commerce websites, streaming services, social media platforms, and more.
Problems Solved
1. Enhancing the quality of recommendations by considering multiple endorsements from different nominators. 2. Improving user engagement by presenting the most relevant endorsements for recommended content items.
Benefits
1. Increased user satisfaction with personalized recommendations. 2. Enhanced user experience through more accurate endorsements. 3. Improved decision-making for users when selecting content items.
Potential Commercial Applications
Optimizing product recommendations in e-commerce platforms for increased sales and customer satisfaction.
Possible Prior Art
One possible prior art could be the use of collaborative filtering techniques in recommendation systems to generate personalized recommendations based on user preferences and behavior data.
What are the potential limitations of this technology?
The technology may face challenges in scalability when dealing with a large number of nominators and recommended content items. Additionally, ensuring the diversity of nominators and avoiding bias in the endorsement selection process could be potential limitations.
How does this technology compare to existing recommendation systems?
This technology sets itself apart by leveraging an ensemble of nominators to provide candidate endorsements, which can lead to more diverse and accurate recommendations compared to traditional recommendation systems that rely on single sources of data or feedback.
Original Abstract Submitted
techniques are disclosed that enable the generation of candidate endorsements for recommended items of content using an ensemble of nominators. various implementations include each nominator in the ensemble providing a candidate endorsement for each recommended item of content. additionally or alternatively, an endorsement is selected to present to the user based on a score determined for each candidate endorsement.
