Dynamic Facets Using Large Language Models for Content Filtering
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Solution Overview
Problem
Existing content management platforms suffer from inaccuracies and inefficiencies in filtering digital content due to broad or generic pre-defined filters, leading to excessive navigation and operational inflexibilities.
Innovation Solution
A dynamic facet system using a large language model generates on-the-fly filters by extracting raw facet data from content items, determining facet content groups, and creating tailored dynamic facets through a facet prompt.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-defined filters are used to narrow down content, then the system provides basic content organization capability, but the filters are too broad or generic resulting in low accuracy
Solution Approach 1:
The system transitions from static pre-defined filters to dynamic facet generation. Facets are created on-the-fly based on the specific content collection being viewed, adapting to different contexts and user needs rather than using fixed generic categories.
Solution Approach 2:
The system automatically generates relevant facets by analyzing the content collection itself, without requiring manual curation or pre-definition of filter categories. The content metadata drives the facet generation process autonomously.
2Measurement precision
If tailored filters are created based on digital content stored on the platform, then the system attempts to improve filter relevance, but the filters remain inaccurate due to generic information
Solution Approach 1:
The system pre-computes facet metadata and content groupings in advance, organizing content by multiple facet dimensions before user interaction. This preliminary organization enables rapid facet generation when users navigate to content collections.
Solution Approach 2:
The system replaces manual filter creation and navigation with automated AI-driven facet generation. The AI analyzes content metadata and automatically creates relevant facets, eliminating the need for users to manually navigate through inaccurate pre-defined filters.
3Productivity
If users navigate back-and-forth through interfaces to locate content items, then the system attempts to find relevant content, but excessive navigation consumes time and resources
Solution Approach 1:
The system adds a new dimensional approach to content organization by creating multi-dimensional facets from content metadata. Instead of linear navigation through single-category filters, users can explore content through multiple facet dimensions simultaneously, rapidly narrowing down results.
4Adaptability or versatility
If existing systems provide pre-defined filters based on popular categories, then the system offers basic organization functionality, but the filters are inconsistent in usefulness across different content collections
Solution Approach 1:
The system applies local quality by generating facets specifically tailored to each content collection's characteristics. Rather than using uniform pre-defined filters across all content, the AI analyzes the specific metadata and properties of each collection to create locally optimized facets that are highly relevant to that particular set of content.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a dynamic facet by using a large language model. For example, the disclosed systems extract raw facet data from a plurality of content items stored in a content management system. In addition, the disclosed systems determine one or more facet content groups by grouping the plurality of content items according to the raw facet data. Further, the disclosed systems generate a facet prompt from the one or more facet content groups. Moreover, the disclosed systems generate a dynamic facet by providing the facet prompt to a large language model.