18299310. CONTENT RECOMMENDATION USING RETRIEVAL AUGMENTED ARTIFICIAL INTELLIGENCE simplified abstract (Microsoft Technology Licensing, LLC)

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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 18299310 titled 'CONTENT RECOMMENDATION USING RETRIEVAL AUGMENTED ARTIFICIAL INTELLIGENCE

    • Simplified Explanation:**

The patent application discusses using 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 through comparison with a plurality of vectors.
  • 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 personalized advertising.

    • Problems Solved:**

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

    • Benefits:**
  • Enhanced user experience through personalized content recommendations.
  • Increased user engagement and satisfaction.
  • Improved content discovery and consumption.
    • Commercial Applications:**

Potential commercial applications include personalized advertising, content curation platforms, and recommendation systems for e-commerce websites.

    • Prior Art:**

Readers can explore prior art related to content recommendation systems, artificial intelligence, and large language models to understand the existing technologies in this field.

    • Frequently Updated Research:**

Stay updated on advancements in artificial intelligence, natural language processing, and content recommendation algorithms to enhance the effectiveness of this technology.

    • Questions about Content Recommendations:**

1. How does this technology improve user engagement with content recommendations? 2. What are the key factors that influence the accuracy of content recommendations in this system?

By incorporating retrieval augmented artificial intelligence, this technology revolutionizes the way content recommendations are provided to users, offering a more personalized and engaging experience across various platforms and industries.


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.

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