Search Rule Analysis via Popular Query Coverage
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Solution Overview
Problem
Users face challenges in obtaining relevant search results due to the difficulty in authoring rules that accurately match user queries with data items, as existing search mechanisms often return results that are not directly related to the user's interests, requiring time-consuming experimentation with additional constraints.
Innovation Solution
A system and method for generating and analyzing rules based on popular query coverage, which suggests candidate aspect-values and determines percentage coverage for queries, domains, aspects, and aspect-value pairs, allowing category managers to create and evaluate rules that improve search relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a search mechanism returns search results covering a wide spectrum of data items, then the coverage is comprehensive, but the relevance to user interests decreases
Solution Approach 1:
The patent applies local quality by extracting and applying specific aspect-values (such as color, brand, size) from data items to create targeted search rules. Instead of treating all search results uniformly, the system identifies specific attributes that matter to users and uses them to filter and rank results, making different parts of the search result set have different levels of relevance based on their matching aspect-values.
Solution Approach 2:
The system changes parameters by transforming unstructured text descriptions into structured aspect-value pairs. By extracting specific attributes (color, brand, size, etc.) from free-text data item descriptions and converting them into structured parameters, the system enables precise filtering and matching while maintaining comprehensive coverage across diverse data formats.
2Manufacturing precision
If a user adds additional constraints to narrow search results, then the relevance improves, but the time required for experimentation increases
Solution Approach 1:
The system performs preliminary action by pre-extracting aspect-values from data item descriptions and storing them in a structured format before search queries are executed. This advance preparation allows the search mechanism to quickly apply filters based on pre-computed attributes rather than requiring users to experiment with constraints in real-time, significantly reducing query experimentation time.
Solution Approach 2:
The system applies self-service by automatically extracting relevant aspect-values from data item descriptions and generating search rules without requiring user intervention. The system autonomously identifies important attributes, creates structured representations, and formulates search constraints, eliminating the need for users to manually experiment with different search parameters.
3Manufacturing precision
If category managers manually author rules to match queries with data items, then the search relevance improves, but the complexity and time required to author rules increases
Solution Approach 1:
The system applies self-service by automatically extracting aspect-values from data item descriptions and generating search rules without requiring manual intervention from category managers. The system autonomously identifies relevant attributes, creates structured aspect-value pairs, and formulates matching rules, eliminating the complex manual rule authoring process while maintaining high matching accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual rule authoring with an automated information processing system. Instead of category managers manually analyzing and creating rules, the system uses text processing and pattern recognition to automatically extract aspect-values and generate search rules, substituting human cognitive work with automated computational processes.
4Manufacturing precision
If manual rule authoring is used to improve search relevance, then the matching accuracy improves, but the productivity of rule creation decreases
Solution Approach 1:
The system performs self-service by automatically extracting aspect-values from data item descriptions and generating search rules without human intervention. This automation maintains high matching accuracy through systematic attribute extraction while dramatically increasing rule creation speed by processing multiple data items simultaneously without manual effort.
Solution Approach 2:
The system enables continuous rule creation by automatically processing data item descriptions as they are added or updated. The aspect-value extraction and rule generation process runs continuously without interruption, maintaining up-to-date search rules that reflect current data while continuously improving productivity through automated batch processing capabilities.
Data Source
AI summary
There are provided methods and systems to analyze rules. The system may analyze the rules by applying the rules to a most popular query. The most popular query may be identified as such by containing a particular keyword. Specifically, a most popular query may contain a keyword that appears more often than other keyword in a sample of queries received over a period of time. Next, the system applies the rules to the most popular query to determine a percentage of coverage of the rules for the most popular query. For example, a particular rule may evaluate TRUE, if the rule identifies a keyword in the query. Next the system generates a percentage of coverage based on a count of the rules that evaluated TRUE and the total number of rules that were applied to the most popular query.