Search Engine Relevancy via Social Term Relationships
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Solution Overview
Problem
Conventional Internet search engines fail to return relevant documents that may not contain the exact search keywords, as they rely solely on keyword matches and social network sharing, missing potentially useful results with related but non-obvious terms.
Innovation Solution
The method involves identifying term relationships within a social network to enhance search relevancy by associating terms based on user interactions and social connections, thereby augmenting document indexing and search queries with non-obvious terms that users have found useful, even if they are not initially present in the documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search engines rely solely on keyword matches and social network sharing, then the search process remains simple and fast, but relevant documents containing non-obvious terms are missed
Solution Approach 1:
The patent introduces term relationship data as an intermediary element that mediates between search queries and document contents. This intermediary layer captures semantic relationships between terms from social network interactions, allowing the search system to match queries with documents based on related terms without requiring direct keyword matches, thereby improving relevancy while maintaining manageable complexity through structured data organization
Solution Approach 2:
The system performs preliminary action by pre-extracting and storing term relationships from social network interactions before search operations. Term relationships are identified and stored in advance from user interactions, document associations, and social network data, so that when a search is executed, the system can immediately leverage these pre-computed relationships without performing complex real-time analysis, thus improving both relevancy and efficiency
2Reliability
If term relationships from social networks are incorporated into document indexing, then search relevancy improves, but the time and computational resources required for indexing increase
Solution Approach 1:
Term relationships are extracted and stored in advance from social network interactions, document associations, and user behaviors before search operations occur. This pre-computation approach allows the indexing system to capture semantic relationships without performing complex real-time analysis during search execution, thereby improving indexing accuracy while reducing the time penalty through batch processing and incremental updates
Solution Approach 2:
The system leverages existing social network interactions, user behaviors, and document associations as self-generated data sources for term relationship extraction. By utilizing data that users and systems already produce through their natural interactions, the patent minimizes additional computational overhead while capturing authentic term relationships, thus improving indexing accuracy without proportionally increasing processing time
Data Source
AI summary
Various embodiments provide for altering relevancy of a document by adding (e.g., to an index associated with the document) one or more term relationships (which may result, for example, in adding one or more non-obvious terms). Other embodiments provide for altering relevancy of a search query by adding to the search query one or more terms based upon one or more determined term relationships (in one example, the added term(s) may be one or more non-obvious terms).