17934854. Online Meta-Learning for Scalable Item-to-Item Relationships simplified abstract (Apple Inc.)

Online Meta-Learning for Scalable Item-to-Item Relationships

Organization Name

Apple Inc.

Inventor(s)

Dimitrios Bermperidis of Seattle WA (US)

Sofia M. Nikolakaki of San Jose CA (US)

Rabi S. Chakraborty of San Jose CA (US)

Rajesh Kumar of Cupertino CA (US)

Chandrasekar Venkataraman of Los Altos CA (US)

Natalia G. Silveira of Campbell CA (US)

Puja Das of San Francisco CA (US)

Online Meta-Learning for Scalable Item-to-Item Relationships - A simplified explanation of the abstract

This abstract first appeared for US patent application 17934854 titled 'Online Meta-Learning for Scalable Item-to-Item Relationships

Simplified Explanation

The patent application abstract describes a distribution platform interface for a seed item landing page, which includes a recommendation section. The recommendations are generated based on a framework that considers the seed app and relationship type, and can handle multiple relationship types.

• The interface is designed to display recommendations on a seed item landing page.
• Recommendations are selected using a framework that analyzes the seed app and relationship type.
• The framework can handle multiple relationship types for generating recommendations.

Potential Applications

• E-commerce platforms
• Content recommendation systems
• Social media networks

Problems Solved

• Providing personalized recommendations to users
• Enhancing user engagement on platforms
• Improving user experience by suggesting relevant items

Benefits

• Increased user interaction with recommended items
• Higher conversion rates for recommended products
• Enhanced user satisfaction with personalized recommendations

Original Abstract Submitted

A distribution platform interface is presented with respect to a seed item landing page. The seed item landing page includes a recommendation section. The items listed in the recommendation section are selected from a framework that is configured to provide recommendations of candidate items based on a given seed app and relationship type, and which is configured to handle multiple relationship types.