LLM Micro-Category Generation for Precise Content Clustering
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Solution Overview
Problem
Existing technologies lack adequate tools to generate specific micro-categories for content and items, leading to broad categories that are irrelevant to user preferences and fail to accurately reflect the nuances of user interests.
Innovation Solution
A machine learning-based approach using a large language model (LLM) to analyze content and items, employing graph-based clustering, non-parametric community detection, and few-shot chain-of-thought prompting to generate and name micro-categories, while leveraging textual and image data for improved accuracy and personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional categorization methods are used, then categories cover broad ranges of content, but the categories become too generic and irrelevant to user preferences
Solution Approach 1:
The patent segments broad content categories into finer micro-categories using machine learning clustering. Instead of having single broad categories like 'movies' or 'music', the system divides them into multiple specialized micro-categories (e.g., 'action movies', 'romance movies', 'pop music', 'classical music') that better match user preferences and consumption patterns.
Solution Approach 2:
The system changes the granularity parameter of categorization from broad to fine-grained by using machine learning models to generate micro-categories. This parameter change enables the system to adapt category specificity dynamically based on user behavior data, transforming generic categories into personalized micro-categories that reflect actual user interests.
2Measurement precision
If more specific micro-categories are created, then category precision improves, but the system complexity increases
Solution Approach 1:
The system employs unsupervised machine learning clustering algorithms that automatically generate micro-categories without requiring manual intervention. The algorithm self-organizes content into meaningful groups based on user consumption patterns, eliminating the need for manual category creation and maintenance while achieving high precision micro-categorization.
Solution Approach 2:
The patent replaces manual categorization mechanics with machine learning-based automatic clustering. Instead of relying on human experts to create and maintain detailed category hierarchies, the system uses computational algorithms to dynamically generate micro-categories, significantly reducing operational complexity while improving category precision.
3Adaptability or versatility
If manual category creation is used, then categories can be customized, but human intervention and time consumption increase
Solution Approach 1:
The system performs preliminary analysis of user consumption data to pre-compute clustering patterns and micro-category structures. By analyzing user behavior patterns in advance and pre-generating micro-categories, the system enables rapid customization without requiring real-time human intervention when users interact with the content delivery system.
Solution Approach 2:
The patent substitutes manual category creation with automated machine learning clustering that processes user data and generates customized micro-categories algorithmically. This mechanical substitution eliminates time-consuming human intervention while maintaining high adaptability to individual user preferences through data-driven category generation.
Data Source
AI summary
Devices, systems, and methods for machine learning-based micro-category generation may include a method including identifying, for a respective cluster of content titles or items, a top-K most representative group of the content titles or items; generating, by a machine learning model, based on a first input prompt, the common theme of the top-K most representative group; generating, by the machine learning model, based on the second input prompt and filtering, a group of content titles or items matching the common theme; generating, by the machine learning model, based on a third input prompt, a name for the group of content titles or items matching the common theme; and presenting the group of content titles or items matching the common theme and the name for the group of content titles or items matching the common theme via a user interface of a streaming media application.