Taxonomy-Aligned Recommendation Generation From Sparse User Input
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Solution Overview
Problem
Conventional machine learning models face challenges in generating domain-specific content that adheres to preexisting taxonomies, require high-quality prompts, and are prone to hallucinations and relevancy issues, especially when dealing with limited or poorly formatted user inputs.
Innovation Solution
A recommendation generation system that utilizes domain-specific data to translate user input into a form operating under a preexisting taxonomy, incorporating a recommendation generator, vector store generator, and generative language model to produce relevant and interpretable recommendations without requiring users to have knowledge of the taxonomy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are used to generate domain-specific content, then the model can produce general predictions, but the content fails to adhere to preexisting taxonomies and suffers from hallucinations and relevancy issues
Solution Approach 1:
The patent introduces an intermediary component that translates user input into taxonomy-aligned representations before processing. This intermediary layer acts as a mediator between the user's natural language input and the machine learning model, ensuring that the generated content adheres to preexisting taxonomies while maintaining domain-specific relevance and reducing hallucinations.
2Manufacturing precision
If high-quality prompts are required for machine learning models, then the model output quality improves, but the ease of operation decreases as users must have knowledge of the taxonomy
Solution Approach 1:
The system performs self-service by automatically translating and structuring user input according to the preexisting taxonomy without requiring user intervention. The translation mechanism autonomously handles the conversion of natural language input into taxonomy-aligned formats, eliminating the need for users to possess domain-specific knowledge while maintaining high output quality.
3Adaptability or versatility
If machine learning models process limited or poorly formatted user inputs, then the system accepts diverse input, but the generated content suffers from relevancy issues and hallucinations
Solution Approach 1:
The patent applies preliminary action by preprocessing and translating user input before it reaches the machine learning model. This preliminary translation step structures the input according to the preexisting taxonomy, ensuring that even limited or poorly formatted inputs are converted into reliable, accurate representations that reduce hallucinations and improve content accuracy.
Data Source
AI summary
Methods, systems, and apparatuses include receiving text input via a user interface for an online system. An embedding is generated based on the text input. Supplemental text is generated using the embedding and a vector store including a standardized content items, the supplemental text having a standardized format. The standardized content items are generated by applying a large language model to a plurality of content items. A prompt is formulated including the supplemental text. A generative language model is applied to the prompt. A recommendation is output by the generative language model based on the prompt. The recommendation is provided to the user interface based on at least the text input.