Category-Based Search Relevance via Taxonomy Affinity
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Solution Overview
Problem
Existing content search techniques primarily rely on word searches and link analysis, failing to accurately match queries with documents based on their broader context and meaning, leading to irrelevant search results.
Innovation Solution
Implementing a category-based search system that creates a taxonomy hierarchy and associates documents with categories, allowing queries to be matched against documents based on category affinities, combined with word search and geographic factors, to generate relevance scores for improved search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If word search techniques are used for content search, then the search system is simple and easy to implement, but the search results fail to accurately match queries with documents based on broader context and meaning
Solution Approach 1:
The search system is segmented into multiple independent scoring modules: word search scoring, category-based scoring, geographic scoring, and link-based scoring. Each module operates independently and contributes to the overall relevance score, allowing the system to maintain simplicity in individual components while achieving high accuracy through combination.
Solution Approach 2:
Multiple search approaches (word search, category matching, geographic location, link analysis) are merged into a unified scoring system. The relevance score is calculated by combining scores from different sources, enabling the system to leverage the strengths of each method while compensating for their individual limitations.
2Measurement precision
If category-based search is implemented, then search relevance is improved by considering broader context, but the system complexity increases due to taxonomy creation and category association
Solution Approach 1:
Documents are pre-associated with categories from a taxonomy during indexing, before search queries are received. This preliminary classification allows the system to quickly retrieve and score documents based on category relevance during actual search operations, avoiding the need for real-time complex analysis.
Solution Approach 2:
Categories serve as an intermediary layer between the query and the documents. Instead of directly comparing query terms with document content, the system translates both into category associations, making the matching process more robust to contextual variations and semantic differences.
3Measurement precision
If multiple scoring factors (word search, category, geographic, links) are combined, then search result relevance is enhanced, but the scoring and ranking process becomes more complex
Solution Approach 1:
The system applies partial scoring to each factor rather than requiring perfect matches across all dimensions. Documents can be ranked highly based on strong performance in one or two factors even if they are weaker in others, allowing the system to achieve good relevance without requiring complex interactions between all scoring components.
Data Source
AI summary
Category affinity may be used as a consideration in providing search results. A taxonomy of substantive categories is created and/or obtained. A corpus of document is compared with the taxonomy to determine the category(ies) with which the documents affine. A query is also compared with the taxonomy to determine the category(ies) with which the query affines. A document may receive a category score based on how well the document's category(ies) match the query's category(ies). This document score may be combined with other scores, such as a text score, a link score, and a distance score, and/or any other factors, to determine an overall relevance score. The relevance score may then be used to rank and present search results.