Generalized Query Generation for Search Result Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search engines face challenges in accurately ranking search results, as they often rely on limited user behavior data and do not effectively utilize patterns of user interaction to enhance document relevance, especially when there is little or no data associated with a specific query.
Innovation Solution
The system generates generalized queries by modifying user-submitted queries through techniques such as replacing terms with synonyms, changing term orders, or removing stop words, and uses aggregated user behavior data from related queries to improve document ranking, even when there is no model data available for the specific query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines rely on limited user behavior data for ranking, then the system complexity remains low, but the relevance accuracy deteriorates
Solution Approach 1:
The patent combines user behavior data from multiple related queries to evaluate a single target document. By merging click-through rates, dwell times, and other behavioral metrics across semantically similar queries, the system accumulates sufficient data to improve relevance accuracy even when individual query datasets are limited.
Solution Approach 2:
The patent creates a universal evaluation mechanism where user behavior data from any related query can contribute to ranking documents for different queries. The generalized quality statistic serves multiple queries simultaneously, allowing the system to leverage data across the entire query corpus rather than being restricted to query-specific data.
2Adaptability or versatility
If search engines use query-specific user behavior data only, then the data processing simplicity is maintained, but the adaptability to different queries deteriorates
Solution Approach 1:
The patent pre-computes semantic relationships between queries and documents before ranking is needed. By establishing query similarity metrics and identifying related queries in advance, the system prepares the data structure needed for generalized evaluation, reducing the complexity of real-time processing while improving adaptability to diverse queries.
Solution Approach 2:
The patent introduces a generalized quality statistic as an intermediary between raw user behavior data and final document ranking. This intermediate metric aggregates and normalizes behavior data from multiple queries, serving as a mediator that simplifies the complexity of processing diverse query-specific data while maintaining adaptability across different search contexts.
3Measurement precision
If search engines generalize queries to leverage related data, then the relevance accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies generalization selectively rather than universally. Instead of generalizing all queries to all possible related queries, the system identifies and processes only the most relevant related queries for each target document, concentrating computational resources on the most impactful relationships while ignoring less significant ones.
Solution Approach 2:
The patent uses partial generalization by considering only a subset of related queries that meet certain relevance thresholds. Rather than exhaustively processing all possible query variations, the system applies generalization to the degree necessary to improve accuracy without incurring excessive computational costs, stopping when diminishing returns are reached.
Data Source
AI summary
Systems, methods and computer program products for generalizing a user-submitted query by forming one or more variants of the user-submitted query to generate one or more other queries, each of the one or more other queries being different from the user-submitted query. A generalized quality of result statistic is derived for a first document from respective data associated with each of the other queries, each respective data being indicative of user behavior relative to the first document as a search result for the associated other query. The generalized quality of result statistic is provided as the quality of result statistic input to a document ranking process for the first document and the user-submitted query.