Query Classifier Confidence Ranking for Search Relevance
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Solution Overview
Problem
Current search engines struggle to provide accurate and relevant search results efficiently, leading to a suboptimal shopping experience for online consumers due to differences in product inventories between retail websites and stores.
Innovation Solution
A query classifier system that predicts multiple categories and sub-categories for a query, ranking them based on a confidence indicator, and modifies the query to prioritize higher confidence categories, ensuring more relevant results are displayed first, thereby enhancing the search experience and potentially increasing sales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search engine returns all search results without prioritization, then the completeness of results is maintained, but the relevance and user satisfaction decrease
Solution Approach 1:
The query classifier performs preliminary categorization of the search query into multiple categories with confidence indicators before the search is executed. This preliminary action allows the system to prioritize which categories to search first, improving result relevance without adding significant complexity during the actual search execution.
Solution Approach 2:
The system changes the parameter of query processing by introducing confidence indicators and category priorities. Instead of treating all categories equally, the system modifies the search parameters to weight categories based on classifier confidence, thereby improving result relevance through parameter optimization.
2Measurement precision
If the query classifier is always applied, then the relevance of search results improves, but the processing time increases
Solution Approach 1:
The system applies partial action by selectively applying the query classifier only when certain conditions are met (e.g., when the query contains specific keywords or patterns). This partial application reduces processing time while maintaining accuracy improvements where they are most beneficial, avoiding unnecessary classifier application for simple or unambiguous queries.
3Measurement precision
If multiple categories are searched with equal priority, then all potential results are captured, but the most relevant results are not prioritized
Solution Approach 1:
The system segments the search process by dividing results into different priority groups based on category confidence indicators. Instead of searching all categories simultaneously with equal weight, the search is segmented into high-priority categories (searched first) and lower-priority categories (searched later or with less weight), improving both relevance ranking and search speed.
Solution Approach 2:
The confidence indicators are calculated in advance as a preliminary step before the actual search execution. This preliminary calculation of category priorities allows the search engine to efficiently allocate resources and search high-confidence categories first, improving productivity while maintaining measurement precision.
4Ease of operation
If the search engine returns results in arbitrary order, then all results are displayed, but user satisfaction and conversion rates decrease
Solution Approach 1:
The system changes the ordering parameter of search results from arbitrary or purely alphabetical ordering to confidence indicator-based ordering. By modifying the sorting parameter to prioritize results from high-confidence categories, the system improves ease of operation and user satisfaction without requiring complex re-ranking algorithms, as the confidence indicators naturally provide the sorting criterion.
Data Source
AI summary
A computer-implemented method for processing a query is disclosed. The method includes receiving, by a server, a query from a user device over a network, the query including one or more keywords; classifying, by the server, the query into one or more categories based on the one or more keywords of the query; determining, by the server, a confidence indicator associated with each of the one or more categories from the classifying; and modifying the query to boost the one or more categories when the confidence indicator associated with the one or more categories for one or more of the keywords of the query is greater than or equal to a confidence level threshold.