Browse Node Creation via Frequent Pattern Mining
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently creating browse nodes for search result pages due to the vast number of combinations of keywords, categories, and key-value pairs, making it prohibitive to automate the creation of browse nodes for every possible combination.
Innovation Solution
The implementation of frequent pattern mining systems and methods to identify and create browse nodes by accessing key-value pairs associated with items, determining combinations that meet a threshold, and selectively creating nodes based on overlap and popularity criteria, allowing for the creation of browse nodes that cover a significant portion of user interests with minimal effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If browse nodes are created for every possible combination of keywords, categories, and key-value pairs, then browse node coverage is improved, but system complexity and computational resources increase prohibitively
Solution Approach 1:
The patent segments the vast search space by identifying and prioritizing frequent patterns (common keyword combinations, category hierarchies, and key-value pair associations) that actually occur in user queries. Instead of creating browse nodes for all possible combinations, the system segments coverage to focus only on high-frequency patterns, thereby reducing complexity while maintaining effective coverage for the majority of user needs.
Solution Approach 2:
The patent applies partial action by creating browse nodes for only the most frequent and relevant patterns rather than exhaustive coverage. The system identifies patterns that meet minimum frequency thresholds and creates nodes selectively, achieving sufficient coverage for common queries without the prohibitive cost of complete coverage. This partial approach balances resource constraints with user needs.
2Adaptability or versatility
If browse nodes are created for every possible combination of keywords, categories, and key-value pairs, then browse node coverage is improved, but computational resources and time increase prohibitively
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing frequent patterns (keyword co-occurrences, category relationships, and key-value associations) during system initialization or periodic updates. This preliminary analysis identifies which browse nodes are most likely to be needed, allowing the system to pre-create only those nodes before actual user queries arrive, thereby avoiding costly on-demand computation for every possible combination.
Solution Approach 2:
The system performs partial computation by calculating frequent patterns only for data that meets minimum occurrence thresholds, rather than analyzing all possible combinations. This selective computation significantly reduces processing time and resource requirements while still capturing the essential patterns that represent the bulk of user search behavior.
3Manufacturing precision
If manual creation of browse nodes is performed, then browse node quality is improved, but labor effort and time increase prohibitively
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate browse nodes through frequent pattern mining of existing search data, category structures, and item attributes. The system autonomously identifies meaningful patterns and creates corresponding browse nodes without requiring manual intervention for each node, thereby maintaining quality through data-driven insights while eliminating the time-consuming manual creation process.
Solution Approach 2:
The system uses feedback from actual user query patterns, click-through data, and search behavior to iteratively improve browse node creation. By analyzing real-world usage data, the system refines its pattern identification and node generation processes, ensuring that automatically created nodes align with actual user needs and maintain high quality comparable to manual creation.
Data Source
AI summary
A browse node page is addressed by its path. As a result, a web crawler may be more likely to find the browse node than the corresponding parameter-based search page. The browse node may be further distinguished from a search result page by the use of a title or header metatag that indicates information about the browse node and distinguishes the browse node from a general search result page. The number of combinations of keywords, categories, and key-value pairs makes the creation of a browse node for every possible combination prohibitive in all but the simplest applications. Methods and systems to identify which search result pages should be converted to browse nodes are disclosed herein.