Auto-Curating Q&A Webpages via Clustering for Search Ranking
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Solution Overview
Problem
Q&A websites typically generate 'thin' content webpages that lack relevance and ranking in search engines, failing to provide users with comprehensive information on a single topic, leading to low visibility and engagement.
Innovation Solution
A computer-implemented method that clusters questions related to a common topic from a Q&A library, removes duplicates and dead content, and aggregates questions into rich content webpages, utilizing click history analysis and user votes to enhance content curation and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Q&A websites generate individual webpages for each question, then each question can be answered independently, but the content becomes 'thin' and lacks comprehensive information, resulting in low search engine ranking
Solution Approach 1:
The patent merges multiple individual question webpages into a single comprehensive topic webpage by clustering related questions together. This consolidation transforms thin individual pages into a rich, comprehensive resource that provides complete information on a topic while maintaining ease of automated generation through the clustering algorithm.
2Ease of operation
If Q&A websites create separate webpages for each question, then user navigation is simple, but search engine visibility and traffic are limited due to low content quality and relevance
Solution Approach 1:
The patent segments questions into clusters based on their semantic relationships and topics. This segmentation allows the system to organize questions into manageable groups that form comprehensive topic pages, improving search engine visibility while maintaining simple user navigation through hierarchical organization.
Solution Approach 2:
By merging multiple related questions into a single comprehensive webpage, the patent creates content that is both easy to navigate and highly visible to search engines. The unified page structure provides comprehensive information while the automated clustering process maintains operational simplicity.
3Productivity
If Q&A websites aggregate multiple questions into rich content webpages, then search engine ranking improves and comprehensive information is provided, but the process complexity increases
Solution Approach 1:
The patent implements self-service through automated clustering algorithms that automatically organize questions into topics without manual intervention. The system self-curates content by analyzing question relationships, generating comprehensive webpages automatically, and maintaining search engine optimization, thereby reducing process complexity despite the enhanced functionality.
Solution Approach 2:
The patent replaces manual content curation mechanisms with automated computational clustering algorithms. This substitution eliminates the need for manual review and organization of questions, reducing process complexity while achieving comprehensive content aggregation that improves search engine ranking.
4Quantity of substance
If Q&A websites include all questions in the library, then content volume is maximized, but duplicate and dead content reduces quality and relevance
Solution Approach 1:
The patent extracts and removes duplicate and dead content from the question library before clustering. This extraction process ensures that only high-quality, relevant questions are included in the final webpages, maintaining content volume while improving reliability through the removal of inferior content.
Solution Approach 2:
The patent applies parameter changes by filtering questions based on quality metrics such as answer count, engagement levels, and relevance scores. This parameter-based filtering ensures that only questions meeting certain thresholds are included, maintaining comprehensive content volume while improving overall content quality and relevance.
Data Source
AI summary
A computer-implemented method of generating rich content webpages from a question and answer (Q&A) library includes providing a topic and one or more seed questions related to the topic. The computing device searches the one or more seed questions against all questions in the Q&A library and identifies questions related to the topic. The computing device clusters the text of the questions related to the topic into a plurality of clusters and then removes substantial duplicates from the plurality of clusters. The computing device generates a rich content webpage by aggregating a question from each cluster onto a single webpage containing the topic.