Category Positioning via Search Query Frequency Analysis
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Solution Overview
Problem
Existing systems face difficulties in determining the proper position for adding new categories in a hierarchical structure, especially when multiple categories are presented, and in assessing whether existing categories are positioned correctly.
Innovation Solution
A processing device that acquires frequency data of category names and keywords from search queries and uses predefined distribution patterns to identify the appropriate position for new categories within the hierarchical structure, utilizing an acquirer and identifier to match query frequencies with pre-associated patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If categories are divided based on the number of items reaching a given value, then the category structure can be automatically maintained, but it becomes difficult to determine where to add new categories in a hierarchical structure and whether existing categories are positioned properly
Solution Approach 1:
The system acquires co-occurrence frequency data from search queries as feedback about user behavior, and uses this feedback to automatically determine category positions. The identifier compares acquired frequency distributions with predetermined patterns to automatically identify proper category positions, eliminating the need for manual determination while maintaining accuracy.
Solution Approach 2:
The category structure automatically adjusts itself by using search query data to determine where new categories should be positioned. The system serves itself by using its own operational data (search queries) to optimize its category structure without external intervention.
2Adaptability or versatility
If multiple categories are presented in a hierarchical structure, then comprehensive classification is achieved, but it becomes difficult to determine where new categories should be added and whether existing categories are positioned properly
Solution Approach 1:
Co-occurrence frequency data from search queries serves as an intermediary that bridges the gap between user behavior and category structure. This intermediary data allows the system to automatically determine category positions without complex manual analysis, simplifying the management of hierarchical category structures.
Solution Approach 2:
The system changes the parameter used for category positioning from manual expert judgment to quantitative co-occurrence frequency data. By transforming the positioning criterion into a measurable parameter (frequency of co-occurrence in search queries), the system can automatically manage complex hierarchical structures.
3Ease of operation
If manual methods are used to determine category positions, then flexibility in category placement is maintained, but the process becomes time-consuming and less accurate
Solution Approach 1:
The system replaces manual mechanical processes (human experts determining category positions) with an automated information processing system that analyzes search query data. This substitution eliminates time-consuming manual work while maintaining or improving accuracy through objective frequency-based analysis.
Solution Approach 2:
The system performs preliminary analysis of search query data to pre-determine category positions before actual category creation or modification. By analyzing co-occurrence frequencies in advance and comparing with predetermined patterns, the system prepares category position recommendations that can be quickly implemented without time-consuming manual deliberation.
Data Source
AI summary
An acquirer acquires, for the on-the path categories situated on the path from the topmost category of a hierarchical structure comprising categories into which products or serves are classified to each of a category of interest and the categories immediately below the category of interest, the frequencies of the names of the on-the-path categories and a keyword co-occurring in a search query given to a search device. An identifier identifies the category of interest as a category candidate immediately above a category of which the name is given by the keyword when the frequencies acquired for the on-the-path categories satisfy a candidate condition associated by the search device.