SYSTEMS AND METHODS FOR GENERATING ALTERNATIVE CONTENT RECOMMENDATIONS

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SYSTEMS AND METHODS FOR GENERATING ALTERNATIVE CONTENT RECOMMENDATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18130893 titled 'SYSTEMS AND METHODS FOR GENERATING ALTERNATIVE CONTENT RECOMMENDATIONS

The abstract describes a computer-implemented method for generating content recommendations based on user data and relationship information.

User data, including content metadata, is obtained for a selected user.
A content recommendation process is performed using the user data and content information to generate initial recommendation candidates.
Relationship information for available content items is generated.
Further recommendation candidates are generated from the initial candidates using the relationship information.

- Key Features and Innovation:**

Utilizes user data and content metadata for personalized recommendations.
Incorporates relationship information to enhance recommendation accuracy.
Automated process for generating content recommendations.

- Potential Applications:**

Content streaming platforms
E-commerce websites
Social media platforms

- Problems Solved:**

Improving user experience by providing relevant content recommendations.
Enhancing content discovery for users.
Increasing user engagement and retention.

- Benefits:**

Personalized user experience
Increased user satisfaction
Higher engagement and retention rates

- Commercial Applications:**

Content recommendation systems can be used in various industries such as streaming services, e-commerce platforms, and social media networks to enhance user experience and drive engagement.

- Questions about content recommendation systems:**

1. How does the system ensure user data privacy while making recommendations? 2. What measures are in place to prevent bias in the recommendation algorithm?

- Frequently Updated Research:**

Stay updated on advancements in machine learning algorithms and data analysis techniques to improve the accuracy and efficiency of content recommendation systems.

Original Abstract Submitted

A computer-implemented method for obtaining one or more recommendation candidates for items of content available via a content distribution system, the method comprising: obtaining user data for a selected user, wherein the user data comprise or represent content metadata associated with user activity; performing a content recommendation process using the user data and content information for the available content to generate one or more initial content recommendation candidates for the user; generating or otherwise obtaining relationship information for the available content items and/or metadata associated with the available content items; generating one or more 10 further content recommendation candidates from the initial content recommendation candidates using the relationship information.