Keyword Category Association Apparatus Using Frequency Thresholds
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Solution Overview
Problem
Existing search systems fail to accurately associate keywords with relevant categories, leading to user inconvenience as categories with more searched products may not necessarily have a high degree of relevance with the keyword, due to manual subjective determination of relevance.
Innovation Solution
An association apparatus that acquires the frequency of category designation with a keyword and registers categories exceeding a threshold for association, allowing categories frequently designated with a keyword to be considered relevant, and hierarchically associating sub-categories when applicable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If categories are displayed in descending order of the number of restaurants searched by the designated keyword, then the quantity of search results is maximized, but the relevance between the keyword and the displayed category deteriorates
Solution Approach 1:
The system collects feedback from user search behaviors by recording the combination of keywords and categories designated together. This feedback data is then processed to calculate association frequencies, which are used to dynamically adjust the display order of categories. The feedback loop ensures that categories frequently selected with specific keywords rise in prominence, resolving the contradiction between quantity and relevance by using actual user behavior data to guide display priorities.
Solution Approach 2:
The system changes the display parameter from a static count-based ordering to a dynamic association frequency-based ordering. By calculating the frequency with which categories are designated together with keywords based on accumulated search data, the system transforms the ordering criterion to reflect both the quantity of results and the relevance to the keyword, thereby resolving the contradiction between these two opposing requirements.
2Measurement precision
If manual extraction and association of keyword-category pairs is performed, then the relevance between keyword and category can be determined, but the complexity of the system increases and subjectivity affects accuracy
Solution Approach 1:
The system performs self-service by automatically extracting and associating keyword-category pairs based on accumulated search data. Instead of requiring manual extraction, the system autonomously analyzes user behavior patterns, calculates association frequencies, and updates category rankings automatically. This eliminates the need for manual intervention while maintaining high relevance accuracy through data-driven insights.
Solution Approach 2:
The system replaces the manual mechanical process of extraction and association with an automated computational process. By substituting human judgment with algorithmic analysis of search data, the system objectively determines keyword-category relevance based on actual usage patterns rather than subjective manual assessment, thereby reducing both system complexity and subjectivity.
3Extent of automation
If the number of designations is used as the criterion for association, then the automation extent increases, but the precision of relevance measurement may deteriorate due to frequency bias
Solution Approach 1:
The system transforms the raw designation count into a normalized association frequency metric that accounts for the total number of times a keyword is designated. By calculating the ratio of category-specific designations to overall keyword designations, the system adjusts the measurement parameter to reflect true relevance rather than absolute frequency, thereby maintaining automation while improving measurement precision.
Data Source
AI summary
To associate a keyword with a category having a high degree of relevance with the keyword. An association apparatus comprises an acquisition means that acquires the number of times when each of a plurality of categories of a search target is designated as a search condition together with a keyword, and a registration means that registers category information indicating the category for which the number of times acquired by the acquisition means is a threshold or more, and the keyword in an associated manner.