18407739. Recommendation Network Using Machine Learning simplified abstract (Capital One Services, LLC)

Recommendation Network Using Machine Learning

Organization Name

Capital One Services, LLC

Inventor(s)

Micah Price of The Colony TX (US)

Avid Ghamsari of Carrollton TX (US)

Qiaochu Tang of Frisco TX (US)

Geoffrey Dagley of McKinney TX (US)

Staeven Duckworth of Oak Point TX (US)

Recommendation Network Using Machine Learning - A simplified explanation of the abstract

This abstract first appeared for US patent application 18407739 titled 'Recommendation Network Using Machine Learning

Simplified Explanation

Methods, systems, and apparatuses are described herein for providing purchase recommendations by analyzing social networks using machine learning. A machine learning model may be trained to select one or more of the first plurality of users. Purchase intention data that indicates an intention of a first user to acquire a type of asset may be received. Social networking data that comprises a plurality of associations between a second plurality of users may be received. Purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users may be received. The trained machine learning model may be provided the data. In return, the trained machine learning model may provide an indication of a second user. A notification may be sent to the second user.

• Machine learning model used to analyze social networks for purchase recommendations
• Trained model selects users based on purchase intention and social networking data
• Provides recommendations based on purchase history of similar users
• Sends notifications to users based on recommendations

Potential Applications

The technology can be applied in e-commerce platforms, social media networks, and online advertising to provide personalized purchase recommendations to users.

Problems Solved

This technology helps users discover products or services they may be interested in based on their social network connections and purchase history, improving user experience and increasing sales for businesses.

Benefits

- Personalized purchase recommendations - Increased user engagement and satisfaction - Higher conversion rates for businesses

Potential Commercial Applications

"Enhancing User Experience and Sales through Social Network Analysis for Purchase Recommendations"

Possible Prior Art

One possible prior art could be collaborative filtering algorithms used in e-commerce platforms to recommend products based on user behavior and preferences.

=== What are the limitations of this technology in providing accurate purchase recommendations? The accuracy of purchase recommendations may be limited by the quality and quantity of data available for analysis, as well as the complexity of user preferences and behavior.

=== How can user privacy be protected while utilizing social network data for purchase recommendations? User privacy can be protected by implementing strict data anonymization and encryption protocols, obtaining user consent for data usage, and adhering to data protection regulations such as GDPR.

Original Abstract Submitted

Methods, systems, and apparatuses are described herein for providing purchase recommendations by analyzing social networks using machine learning. A machine learning model may be trained to select one or more of the first plurality of users. Purchase intention data that indicates an intention of a first user to acquire a type of asset may be received. Social networking data that comprises a plurality of associations between a second plurality of users may be received. Purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users may be received. The trained machine learning model may be provided the data. In return, the trained machine learning model may provide an indication of a second user. A notification may be sent to the second user.