Search Relevance Scoring via User Highlight Analysis
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Solution Overview
Problem
Existing methods for organizing and ranking internet documents are either expensive due to manual analysis, prone to inconsistencies, vulnerable to metadata manipulation, and do not account for user relevance, as they rely on human judgment or author-controlled metrics.
Innovation Solution
A method that analyzes user-generated highlights and associated comments to determine a document's relevance score, using algorithms that consider the number and quality of highlights and comments, to rank documents in search results independently of author influence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and human categorization are used, then categorization accuracy and relevance can be maintained, but the cost and time required increase significantly
Solution Approach 1:
The system enables users to automatically categorize and tag web pages through intuitive interaction interfaces. Users can drag and drop pages into categories, add tags, and organize content without requiring manual analysis by professionals, thus maintaining accuracy while reducing time investment.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems that use algorithms to categorize and rank web pages based on extracted features, reducing human time investment while maintaining or improving categorization accuracy through consistent algorithmic application.
2Loss of time
If automated analysis by software agents is used, then the time and cost for analysis are reduced, but the reliability of relevance determination deteriorates due to metadata manipulation
Solution Approach 1:
The system incorporates feedback mechanisms where users can rate, comment on, and re-categorize web pages. This feedback loop allows the system to learn from user preferences and adjust its relevance determination algorithms, preventing manipulation by continuously adapting to actual user behavior patterns rather than relying solely on static metadata.
Solution Approach 2:
The patent changes the parameters used for relevance determination from relying solely on author-controlled metadata to incorporating user-generated signals such as usage patterns, comments, and interaction data. This parameter shift makes the system resistant to metadata manipulation while maintaining automation.
3Extent of automation
If relevance is determined by incoming links from other documents, then automated ranking is achieved, but the perspective of end-users is lost
Solution Approach 1:
The system serves multiple functions: it automatically processes web pages through algorithms while simultaneously capturing user preferences through interactions. This multi-functionality allows the system to maintain automation for large-scale processing while incorporating user-centric relevance assessment through integrated user feedback mechanisms.
Solution Approach 2:
Users actively participate in the relevance determination process through intuitive interfaces that allow them to rate, comment on, and organize web pages. This self-service approach gives end-users direct control over relevance assessment while the system maintains automated processing capabilities for scalability.
Data Source
AI summary
A method for determining the significance of a web page, or a portion thereof, is disclosed. Accordingly, a search engine or some other application analyzes user-generated highlights (as well as user-provided comments associated with the highlights) of a document to determine a document relevance score (e.g., Highlight Rank) for the document containing the user-generated highlights. The particular algorithm for determining the document relevance score will vary depending upon the particular implementation, but may generally be based upon an analysis of the number and quality of user-generated highlights and associated comments within a document. Based on this analysis, the search engine assigns the document a document relevance score, which is used for processing the document in accordance with instructions associated with a search query. For example, the document relevance score may be used in selecting and ordering documents returned in search results for a particular search query.