LLM Persona Matching for Accurate App Tagging at Scale
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Solution Overview
Problem
Manual labeling of mobile applications for subjective categorization is impractical due to scalability, consistency, and accuracy issues, and automated heuristics often fail to align with human expert labeling.
Innovation Solution
A graph augmentation system using supervised machine learning models and large language models to automatically generate and update tags for applications, incorporating signals and confidence levels, and respond to queries based on the graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used for application categorization, then labeling accuracy and consistency are improved, but scalability and productivity deteriorate
Solution Approach 1:
The patent segments the labeling task into multiple components: signal extraction from application data, feature representation, and hierarchical tag generation. The supervised learning model processes applications in batches with progressive training, dividing the large-scale labeling problem into manageable training iterations that improve both accuracy and scalability
Solution Approach 2:
The patent introduces an intermediary supervised learning model that acts as a bridge between raw application signals and final tags. The model translates application signals into meaningful features, which are then used to generate accurate tags at scale, resolving the contradiction between manual accuracy and automated scalability
2Productivity
If automated heuristics are used for labeling, then productivity is improved, but labeling accuracy and alignment with human expert judgment deteriorate
Solution Approach 1:
The patent replaces simple mechanical heuristics with a supervised learning model that learns from labeled training data. Instead of rigid rule-based systems, the model uses statistical patterns and feature representations to make labeling decisions, achieving both automation and accuracy by substituting mechanical processing with learned intelligence
Solution Approach 2:
The patent changes the parameters of the labeling system by using learned feature representations and confidence scores instead of fixed heuristic thresholds. The model adjusts its decision parameters based on training data, allowing it to maintain high productivity while improving accuracy through adaptive parameter optimization
3Measurement precision
If more signals are extracted from applications, then tagging accuracy is improved, but system complexity and processing time worsen
Solution Approach 1:
The patent extracts only the most relevant signals from application data using a curated signal extraction process. Instead of processing all possible application attributes, the system identifies and extracts key signals that are most predictive of accurate tagging, reducing system complexity while maintaining or improving tagging accuracy through selective extraction
Data Source
AI summary
A graph includes nodes representing applications and tags describing subjective qualities of the applications. The system responds to queries for user personas by using an LLM to match the persona to applications in the graph. The system receives a natural language query describing a persona. The system generates a prompt for an LLM based on the query and provides the prompt to the LLM for execution. The system receives, as output from the LLM, candidate applications. The system inputs the candidate applications into a classifier trained to classify candidate applications into known applications, applications that already exist in a graph. The system receives, as output from the classifier, known applications. The system determines, for each known application, a quality score of the known application and determines that the quality score exceeds a quality score threshold. In response, the system provides the known applications for display at a user interface.