Microsoft technology licensing, llc (20240346566). CONTENT RECOMMENDATION USING RETRIEVAL AUGMENTED ARTIFICIAL INTELLIGENCE simplified abstract

CONTENT RECOMMENDATION USING RETRIEVAL AUGMENTED ARTIFICIAL INTELLIGENCE

Organization Name

microsoft technology licensing, llc

Inventor(s)

Yinghua Qin of Redmond WA (US)

CONTENT RECOMMENDATION USING RETRIEVAL AUGMENTED ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240346566 titled 'CONTENT RECOMMENDATION USING RETRIEVAL AUGMENTED ARTIFICIAL INTELLIGENCE

• • Simplified Explanation:**

The patent application describes a system that uses retrieval augmented artificial intelligence to provide content recommendations to users based on their contextual information.

• • Key Features and Innovation:**
• Generation of a first feature vector based on user contextual information.
• Determination of second feature vectors by comparing them to the first feature vector.
• Retrieval of content items corresponding to the determined second feature vectors.
• Generation of an augmented prompt based on user contextual information and retrieved content items.
• Requesting a content recommendation from a large language model based on the augmented prompt.

• • Potential Applications:**

This technology can be applied in various industries such as e-commerce, content streaming platforms, social media, and online advertising to enhance user experience and engagement.

• • Problems Solved:**

This technology addresses the challenge of providing personalized content recommendations to users based on their unique preferences and contextual information.

• • Benefits:**
• Improved user engagement and satisfaction.
• Enhanced content discovery and recommendation accuracy.
• Increased user retention and loyalty.

• • Commercial Applications:**
• "Enhanced Content Recommendation System for E-commerce Platforms"
• This technology can revolutionize the way online retailers recommend products to customers, leading to increased sales and customer satisfaction.

• • Prior Art:**

Researchers can explore prior art related to content recommendation systems, artificial intelligence, and large language models to understand the evolution of this technology.

• • Frequently Updated Research:**

Stay updated on advancements in artificial intelligence, natural language processing, and content recommendation algorithms to leverage the latest innovations in this field.

• • Questions about Content Recommendation Systems:**

1. How does this technology improve user engagement compared to traditional content recommendation systems? 2. What are the potential privacy concerns associated with using retrieval augmented artificial intelligence for content recommendations?

Original Abstract Submitted

systems, methods, apparatuses, and computer program products are disclosed for using retrieval augmented artificial intelligence to provide content recommendations. a first feature vector is generated based at least on user contextual information. second feature vectors are determined based on a comparison of the first feature vector to a plurality of second feature vectors. content items corresponding to the determined second feature vectors are retrieved. an augmented prompt generated based on the user contextual information and the retrieved content items is provided to a large language model to request a recommendation. a content recommendation is received from the large language model based on the augmented prompt.