LLM Content Personalization via User Segment Examples
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Solution Overview
Problem
Existing technologies struggle to personalize text-based content effectively for different user segments, as adapting textual style does not sufficiently resonate with users' specific interests, leading to inefficient content creation and increased computing resource usage.
Innovation Solution
The technology facilitates the efficient and effective generation of alternative examples for content in an automated manner, using machine learning models to identify source examples and generate tailored alternative examples that correspond to different user segments, thereby personalizing content without the need for continuous content creation and review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual content creation and review processes are used to personalize content for different user segments, then content relevance to user interests is improved, but productivity decreases and loss of time increases
Solution Approach 1:
The patent replaces the mechanical manual content creation process with an automated machine learning system. The ML model generates alternative examples for different user segments automatically, substituting human writers and editors with an algorithmic system that can produce personalized content at scale without manual intervention for each user segment.
Solution Approach 2:
The system enables content to serve itself by automatically generating alternative examples for different user segments without requiring manual review or adjustment. The ML model autonomously analyzes the original content and produces tailored variations for specific user interests, making the content personalization process self-sufficient.
2Manufacturing precision
If manual content generation and validation processes are used to create personalized content, then content quality is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-generating alternative examples for different user segments automatically. The ML model is trained beforehand to understand content patterns and can rapidly generate quality alternative examples when needed, eliminating the need for time-consuming manual creation and validation processes for each content piece.
3Productivity
If automated machine learning processes are used to generate alternative examples, then productivity is improved, but use of energy increases
Solution Approach 1:
The system applies partial action by generating alternative examples only for specific user segments when needed, rather than creating all possible variations continuously. The ML model processes and generates content on-demand based on user interest identification, reducing unnecessary computing resource consumption while maintaining high productivity for relevant content personalization.
Data Source
AI summary
Methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating alternative examples for content. In embodiments, a source example prompt is obtained at a large language model. The source example prompt includes text associated with a source content and an instruction to generate a source example from the text associated with the source content. Using the large language model, the source example that represents an entity and corresponding context from the text is generated. Thereafter, the source example and a set of user segments are provided as input into the large language model to generate alternative examples associated with the source content. Each alternative example corresponds to a user segment of the set of user segments. Based on a particular user segment associated with a user interested in the source content, an alternative example corresponding to the particular user segment is provided for display.