AI Knowledge Graph Maps Qualitative Terms to Quantitative Ranges
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Solution Overview
Problem
Traditional searching methods are inefficient and impersonal, as they struggle to understand qualitative terms and provide relevant results due to technical limitations, leading to time-consuming browsing and irrelevant outcomes, especially with the vast scale of online items.
Innovation Solution
An intelligent personal assistant system using scalable AI that learns from user interactions to associate qualitative terms with quantitative ranges, allowing for personalized and relevant search results by understanding user intent and filtering search results accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional browsing engines are used to search for products, then users can access a wide range of items, but users spend too much time browsing and get irrelevant results
Solution Approach 1:
The system collects user interaction data (clicks, purchases, browsing behavior) and uses it to refine and update quantitative ranges for qualitative terms. This feedback loop enables the system to continuously improve its understanding of user intent and adjust search results accordingly, reducing irrelevant results and browsing time while maintaining comprehensive coverage
Solution Approach 2:
The system dynamically adjusts the quantitative ranges associated with qualitative terms based on user interactions and market changes. By changing these parameters (the numerical ranges) based on accumulated data, the system optimizes search results to better match user expectations without requiring users to manually filter through irrelevant items
2Ease of operation
If conventional searching tools are used, then users can perform basic searches, but users cannot effectively communicate their intent using qualitative terms
Solution Approach 1:
The system introduces an intermediary layer (the AI framework with quantitative ranges) between the user's qualitative terms and the product database. This intermediary translates subjective descriptions like 'affordable' or 'premium' into objective numerical ranges, enabling effective communication of user intent without requiring users to understand technical search parameters
Solution Approach 2:
The system replaces traditional keyword-matching mechanics with an AI-based semantic understanding system. Instead of mechanically searching for exact keyword matches, the system uses machine learning to interpret qualitative terms and map them to appropriate quantitative ranges, preserving user intent information that would be lost in conventional searching
3Productivity
If traditional search+refinements+browse methods are used, then users can filter results, but these methods are not useful at scale with billions of items
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing quantitative ranges for qualitative terms based on user interactions before searches are executed. This advance preparation allows the system to quickly apply appropriate filters during searches without requiring complex real-time computations, maintaining high efficiency even with billions of items
Solution Approach 2:
The system creates a universal translation layer that works across all product categories and search scenarios. The same AI framework and quantitative range mechanism handles diverse qualitative terms (e.g., 'cheap,' 'expensive,' 'budget-friendly') uniformly across different product types, simplifying the search system while maintaining effectiveness at scale
Data Source
AI summary
In various example embodiments, a system and method for applying a quantitative range for qualitative terms. In one example, a method includes gathering user interactions, identifying a qualitative term included in the user interactions, building an electronic knowledge graph that associates the qualitative term with a quantitative range for the product identified using the qualitative term according to values in the user interactions that include the qualitative term, receiving a query for the product that includes the qualitative term, and performing, in response to receiving the query, a search that limits results according to the quantitative range stored in the knowledge graph.