Content Matching via Document Feature Vectors
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Solution Overview
Problem
Current search engines and content platforms struggle to accurately identify and display related content due to noise in vast amounts of information, lack of relevance measurement in document clustering, and cumbersome blog navigation.
Innovation Solution
A content matching system that analyzes raw text through noise word removal, stemming, phrase discovery, and link analysis to create document feature vectors, adjusts scores based on word commonality, and compares vectors to determine related articles, providing a list of closely matching content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If document clustering is used to group related articles, then the quantity of displayed content increases, but the measurement precision of relevance is insufficient
Solution Approach 1:
The patent transforms the relevance assessment from a binary or crude grouping into a multi-dimensional parameter system using document feature vectors. Each document is represented by vectors capturing semantic content, link structure, and formatting characteristics. The system calculates similarity between vectors to determine relevance, transforming the vague concept of 'relatedness' into precise mathematical measurements that enable both high-volume display and accurate relevance assessment.
2Ease of operation
If blogs are organized by author rather than content, then the ease of operation for authors is improved, but the difficulty of detecting and measuring content relevance increases
Solution Approach 1:
The patent introduces document feature vectors as an intermediary layer between the author-oriented blog structure and the user's content search needs. Instead of requiring users to manually navigate author-based blogs or requiring authors to reorganize content, the system automatically generates feature vectors that capture the semantic essence of each post. These vectors serve as mediators that enable relevance detection and content matching without disrupting the existing author-friendly organization.
3Manufacturing precision
If noise word removal and text analysis are applied, then the purity of content matching is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the complex text processing task into distinct, manageable components: noise word removal, stemming, phrase discovery, link analysis, and formatting analysis. Each component generates specific features that contribute to the overall document feature vector. This segmentation allows the system to achieve high matching accuracy through multiple specialized processing stages rather than a single monolithic complex algorithm, making the system more maintainable and adaptable.
Data Source
AI summary
Various technologies and techniques are disclosed that improve the identification of related content. An article for which to identify matching content is received or selected. The raw text of the article is analyzed to reduce the raw text to a core set of words, and the results are stored in a document feature vector array. The formatted text of the article is analyzed and vector array scores are updated based on the formatting. Anchor text words for documents that link to the article are added to the vector array. Articles linking to and from the particular article are identified and added to the vector array as appropriate. Transformations are performed, such as to adjust the vector scores based on how common or generic the words are. Vector arrays are created for other potentially related documents. The vectors are compared to determine how related they are to each other.