Content Prioritization via Citation Graph Analysis
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Solution Overview
Problem
Current search engines face limitations in finding relevant content without keywords, timely content, and personalized results, as they rely on resource-intensive algorithms like Page Rank that fail to distinguish between trustworthy and untrustworthy links, and require user ratings, which can be burdensome.
Innovation Solution
A system that aggregates and analyzes individual indications of relevance, using a selection acquisition subsystem, scoring engine, and recommendation engine to identify and score trusted content sources based on customizable criteria, without requiring explicit user ratings, and provides personalized content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Page Rank algorithm is used to prioritize content, then content accessibility is improved, but resource consumption increases and timely content is not captured
Solution Approach 1:
The patent extracts the essential function of content prioritization from the full Page Rank algorithm, focusing only on citation graph analysis for trusted content sources rather than processing the entire Internet. This selective extraction maintains prioritization capability while significantly reducing computing resource requirements.
Solution Approach 2:
The patent segments the content prioritization task into two parts: (1) identifying trusted content sources using citation graphs, and (2) ranking content based on those trusted sources. This segmentation allows the system to avoid processing all Internet content while still achieving effective prioritization.
2Productivity
If Page Rank algorithm is used to prioritize content, then content accessibility is improved, but personalized results are not achieved
Solution Approach 1:
The patent introduces dynamic personalization by allowing users to specify trusted content sources or topics of interest. The system then dynamically adjusts the citation graph analysis to prioritize content from these user-defined sources, enabling personalized results while maintaining the efficiency of the simplified prioritization algorithm.
3Device complexity
If all links are considered equal in Page Rank, then simplicity is maintained, but trustworthiness discrimination is lost
Solution Approach 1:
The patent applies local quality by differentiating the treatment of links based on their source. Links from trusted content sources (identified through citation graph analysis) are weighted differently than links from untrusted sources. This maintains relative algorithm simplicity while achieving reliable trustworthiness discrimination.
4Adaptability or versatility
If user ratings are required for content evaluation, then personalization is improved, but user burden increases
Solution Approach 1:
The patent implements self-service by allowing the system to automatically identify and prioritize content based on user-specified trusted sources without requiring continuous user ratings. Users simply define their preferences once, and the system autonomously performs citation graph analysis and content ranking, eliminating the need for ongoing manual evaluation.
Data Source
AI summary
The system provides a technique for finding relevant content and content sources based on the aggregation and analysis of individual indications of relevance. The system identifies and provides selections of relevant content. It may comprise a selection acquisition subsystem, a selection network repository subsystem, a scoring engine, and a recommendation engine, and is used to generate sources of content comprising sets of prioritized links directed to a topic or community of interest.