Webpage Quality Evaluation Using Association Data Signals
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Solution Overview
Problem
Existing search engines struggle to accurately evaluate the quality of webpages without accessing their content, affecting user retrieval experiences.
Innovation Solution
A method and apparatus that evaluate webpage quality using target association data, such as link-related data and user feedback, to determine the quality of index data without accessing the webpage content, employing machine learning models like LSTM and CNN for feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If webpage content is accessed to evaluate quality, then evaluation accuracy is improved, but evaluation speed and efficiency deteriorate
Solution Approach 1:
The patent extracts and utilizes association data (links, user feedback, metadata) from the webpage ecosystem without accessing the actual webpage content. This extraction approach maintains evaluation capability while avoiding the time-consuming process of content analysis, thus resolving the contradiction between accuracy and speed.
Solution Approach 2:
The patent introduces association data as an intermediary to evaluate webpage quality indirectly. Instead of directly analyzing webpage content, the system uses external indicators such as incoming links, user feedback, and metadata as mediators to infer quality, achieving both speed and accuracy.
2Reliability
If webpage content is accessed to evaluate quality, then comprehensive quality assessment is improved, but computational resources and time consumption increase
Solution Approach 1:
The patent extracts quality indicators from the webpage's association data environment rather than analyzing the content itself. This includes extracting link quality metrics, user feedback patterns, and metadata information, which provides comprehensive quality assessment without the time cost of content processing.
Solution Approach 2:
The patent performs preliminary evaluation using association data that is already available in the system, such as pre-collected link information and user feedback. This preliminary assessment action avoids the need for time-consuming content analysis while still providing reliable quality indicators.
3Productivity
If association data is used to evaluate webpage quality without accessing content, then evaluation efficiency is improved, but evaluation accuracy may deteriorate
Solution Approach 1:
The patent uses multiple types of association data (links, user feedback, metadata) as intermediaries to indirectly measure webpage quality. By combining multiple intermediary indicators, the system maintains evaluation accuracy while achieving high efficiency through content-free assessment.
Solution Approach 2:
The patent creates a composite evaluation metric by combining multiple types of association data including link quality, user feedback, and metadata. This composite approach to quality assessment maintains precision by using multiple indicators while preserving efficiency through avoiding content analysis.
Data Source
AI summary
A method of evaluating data, a method of training an evaluation model, an electronic device, and a storage medium are provided, and relate to a field of a computer technology, in particular to fields of intelligent search and deep learning technologies. The method of evaluating data includes: acquiring, in response to a request for identifying a quality of index data to be identified, target association data of a target webpage corresponding to the index data to be identified, wherein the target webpage is a webpage having an unknown web content, and the target association data indicates a quality of the target webpage corresponding to the index data to be identified; and obtaining, based on the target association data, a quality evaluation result for the index data to be identified.