Search Engine Content Filter Using Attentive Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack effective systems for predicting and analyzing the spread of contagious phenomena across online networks, such as news stories or products, and identifying influential authors and readers, which is crucial for targeted advertising and communication strategies.
Innovation Solution
The development of attentive clustering methods that construct online author networks, partition them into clusters based on linking history and citation profiles, and collect data to analyze and visualize the flow of content, enabling the prediction of behavior and growth of contagious phenomena.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search engines index all web content, then comprehensive information coverage is achieved, but information quality and relevance deteriorate due to spam, malware, and low-quality content
Solution Approach 1:
The patent segments the web content into quality tiers (spam, malware, low-quality, high-quality) and applies different indexing strategies to each segment. Search engines can selectively index only high-quality content while maintaining comprehensive coverage options, resolving the contradiction between quantity and reliability.
Solution Approach 2:
Different quality standards and indexing approaches are applied to different segments of web content based on their characteristics. High-quality academic and news content receives preferential treatment with full indexing, while spam and malware are excluded or flagged, achieving both comprehensive coverage and high reliability.
2Reliability
If search engines filter out spam and low-quality content, then information quality improves, but information coverage deteriorates
Solution Approach 1:
The search engine implements dynamic quality assessment that adapts filtering thresholds based on user preferences, query context, and content characteristics. Users can adjust the aggressiveness of filtering to balance quality and coverage according to their needs, resolving the static contradiction between these two parameters.
3Reliability
If search engines manually curate high-quality content, then information quality improves, but system complexity and costs increase
Solution Approach 1:
Content sources such as academic journals and news organizations self-identify as high-quality through standardized metadata and registration processes. The search engine automatically recognizes and prioritizes these self-declared quality sources without requiring manual curation, reducing system complexity while maintaining high information quality.
Solution Approach 2:
The system implements feedback loops where user interactions, citation patterns, and engagement metrics continuously inform quality assessment algorithms. This automated feedback mechanism replaces manual curation with adaptive, data-driven quality evaluation, reducing operational complexity while sustaining high information standards.
4Productivity
If search engines prioritize speed and scalability, then system performance improves, but ability to analyze and filter content quality deteriorates
Solution Approach 1:
Quality assessment and filtering rules are pre-computed and cached during off-peak hours. Content is evaluated against predetermined quality criteria before being added to the index, allowing rapid retrieval without real-time analysis. This preliminary action enables fast search performance while maintaining sophisticated quality filtering capability.
Data Source
AI summary
Computerized search methods and systems generally include presenting, to a user, a computer interface for specifying one or more search terms for a search query and presenting at least one selectable item corresponding to at least one of art M score and a cluster focus index (CFI) score filter for the search query. The methods and systems include generating an amended search query based on a selected item; and performing a search using the amended search query. The M score is calculated using the formula M score=count (alpha)+CFI (1-alpha), where the count is the overall number of members on a cluster focus map that has engaged with a target.