Search Engine Relevance Thresholding for SERP Display
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search engines often provide users with a multitude of irrelevant results, making it difficult for users to quickly find the most relevant information they are searching for, as they display all search results even when a highly relevant document is identified.
Innovation Solution
A method and system that determines a likelihood parameter for search results, where if the parameter is above a threshold, only the most relevant document is displayed, and if below, the general Search Engine Results Page (SERP) including the most relevant document and other documents is shown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all search results are displayed to ensure comprehensive information coverage, then users have access to multiple documents, but users are overwhelmed by irrelevant results and cannot quickly find the most relevant information
Solution Approach 1:
The patent extracts the most relevant document from the search results based on a likelihood parameter. When the likelihood exceeds a threshold, only the most relevant document is displayed, separating it from other potentially irrelevant results. This extraction principle resolves the contradiction by presenting only the high-relevance information needed by users.
Solution Approach 2:
The patent applies partial action by selectively displaying either only the most relevant document or a limited set of top results based on the likelihood parameter, rather than displaying all search results. This partial display approach maintains information relevance while improving user efficiency by showing only what is necessary.
2Ease of operation
If only the most relevant document is displayed to improve user efficiency, then users can quickly access information, but users may miss other potentially useful documents
Solution Approach 1:
The patent dynamically adjusts the search result display based on the likelihood parameter. When likelihood is high, only the most relevant document is shown; when likelihood is low or uncertain, multiple documents including the most relevant one are displayed. This dynamic adaptation resolves the contradiction by balancing efficiency and completeness based on result quality.
Solution Approach 2:
The patent changes the display parameter (number of results shown) based on the likelihood parameter value. The system transitions between displaying a single document and displaying multiple documents according to the calculated likelihood, thereby adapting information completeness to the quality of search results.
3Loss of information
If the search engine displays multiple documents to maintain result completeness, then all information is available, but the system complexity increases due to result ranking and filtering
Solution Approach 1:
The patent extracts the most relevant document using a likelihood parameter calculation, simplifying the complex task of evaluating multiple documents. By focusing on extracting and evaluating only the top result against a threshold, the system reduces processing complexity while maintaining the ability to present complete results when needed.
Data Source
AI summary
There is provided a method for determining a search response to a search query associated with a user. The method may be executable at a server. The method comprises responsive to a receipt of the search query, determining the most relevant document to the search query; determining a likelihood parameter indicative of how likely the most relevant document is to satisfy the search query; in response to the likelihood being above a threshold, displaying exclusively the most relevant document; in response to the likelihood being below the threshold, displaying the general SERP including the most relevant document and other documents.