Search Result Display Adaptation for Query Quality
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Solution Overview
Problem
Conventional search engines often overwhelm users with irrelevant information, especially when search queries are broad, contain spelling mistakes, or are imprecise, leading to inefficient information retrieval.
Innovation Solution
A system that analyzes relevancy metrics to categorize search queries, adjusting the display of search results and providing feedback or suggestions to improve query quality, such as adjusting the number of results shown or running a spell check, based on determined categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed number of search results (e.g., top 100) are displayed regardless of query quality, then the system is simple to operate, but users waste time reading irrelevant information and may miss relevant results
Solution Approach 1:
The search result display system dynamically adjusts the number of results shown based on the quality assessment of the search query. Instead of displaying a fixed number of results, the system modifies display parameters in real-time according to query characteristics, making the system adaptive rather than static.
Solution Approach 2:
The system provides feedback to users about the quality of their search queries by analyzing relevancy metrics and communicating this information back to users. This feedback loop enables users to understand why certain numbers of results are displayed and how to improve their queries for better results.
2Loss of information
If relevancy metrics are displayed to help users interpret result quality, then users can make informed decisions about their searches, but users may still fail to understand or use this information effectively
Solution Approach 1:
The system changes the parameter of information presentation by not just displaying raw relevancy metrics but by translating these metrics into actionable insights and suggestions. The system modifies how quality information is communicated, transforming abstract metrics into concrete guidance for users.
Solution Approach 2:
The system acts as an intermediary between the complex relevancy metrics and the user by providing interpreted information and suggestions. Rather than exposing users directly to raw metrics, the system mediates this information through user-friendly explanations and actionable recommendations.
3Reliability
If the system provides detailed feedback and suggestions for query improvement, then users can learn to conduct better searches, but the system becomes more complex and requires more processing
Solution Approach 1:
The system applies partial action by providing feedback and suggestions only when the search query quality falls below certain thresholds. Rather than analyzing and providing feedback on every single query, the system selectively intervenes when improvement is needed, reducing unnecessary processing while maintaining reliability.
Solution Approach 2:
The feedback system is segmented into multiple components that analyze different aspects of query quality independently. By dividing the analysis into separate modules (e.g., spelling check, relevance analysis, completeness assessment), the system manages complexity through modular design while providing comprehensive feedback.
Data Source
AI summary
In various embodiments, the present invention provides methods and systems for categorizing the quality of a search by analyzing the relevancy numbers associated with the search results. The relevancy numbers are compared to established patterns to categorize the quality of the search query. Based on this categorization, the system alters the display parameters of the results, such as the number of results to display, the message to display to the user, or in some embodiments, a subsequent action the system executes.