Local Content Indexing via Segmented Search Architecture
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Solution Overview
Problem
Current internet search engines face challenges in effectively ranking local content due to their general algorithms, which often bury local content on subsequent search pages, making it difficult for users to discover geographically relevant information without guessing appropriate search terms and resulting in low click-through rates.
Innovation Solution
A system and method utilizing geographic information and machine learning to create self-editing content streams, which automatically discovers and curates local content by bootstrapping initial terms, building a local content corpus, and dynamically ranking documents based on local entity weightings, enabling targeted content delivery to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search engine algorithms are used to index and rank web pages, then general search coverage is improved, but local content visibility deteriorates
Solution Approach 1:
The patent segments the search index into multiple levels: a global index for general search coverage and local indexes for geographically-specific content. This allows the system to maintain broad search capabilities while simultaneously optimizing local content visibility through dedicated local indexing structures.
Solution Approach 2:
The patent implements local quality by creating geographically-aware search results that prioritize locally-relevant content for users based on their location. The system adjusts ranking algorithms to give higher weight to local businesses and services, ensuring that search results reflect the specific needs and characteristics of local communities rather than applying uniform ranking across all regions.
2Quantity of substance
If search engines index an extremely large number of web pages, then comprehensive content coverage is improved, but algorithm effectiveness deteriorates
Solution Approach 1:
The patent extracts and separates local content from the global web index by creating dedicated local indexes. This extraction process removes the complexity of processing entire web pages for local search purposes, allowing the system to maintain comprehensive content coverage while simplifying the algorithms used for local content retrieval and ranking.
Solution Approach 2:
The patent performs preliminary action by pre-processing and organizing content into local categories and geographic groupings before the actual search occurs. This advance organization of content into structured local indexes reduces the computational complexity during search operations, enabling effective algorithms even with extensive content coverage.
3Ease of operation
If local content is ranked lower in search results, then general search fairness is improved, but user click-through rate deteriorates
Solution Approach 1:
The patent implements dynamic ranking that adapts based on user location and search context. Rather than applying static ranking rules, the system dynamically adjusts result ordering to balance fairness with effectiveness, prioritizing local content when geographically relevant while maintaining overall search fairness through transparent and consistent ranking criteria that consider multiple factors including proximity, relevance, and user preferences.
Data Source
AI summary
A method for quantifying localness of content can be organized into several stages of information acquisition and processing, with each stage focusing on filtering or qualifying content based on geographic information relevant to a place. The method may comprise bootstrapping an initial set of terms for the place, building a local content corpus for the place utilizing the initial set of terms, and populating an index with information from documents in the local content corpus. In response to a request about the place, a query is formed and provided to the index for retrieving local content relevant to the place.