Search Ranking Using Semantic and Quantitative Vector Scores
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Solution Overview
Problem
Current user interfaces for conducting local searches over networks, such as the internet, lack effective methods for ranking geographically relevant information, often relying on simplistic text matching rather than semantic relevance and location-specific scores.
Innovation Solution
A system that calculates a qualitative semantic similarity score and a general quantitative score for data entries, combining them into a vector score to rank results based on semantic relevance and location proximity, ensuring that distance and rating scores are considered alongside semantic similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simplistic text matching is used for ranking search results, then the system complexity is reduced and processing speed is improved, but the accuracy and relevance of search results deteriorate
Solution Approach 1:
The ranking system is segmented into multiple independent scoring components: a qualitative semantic similarity score component and a quantitative score component (which includes distance score, rating score, and other factors). Each component operates independently to evaluate different aspects of data entry relevance, allowing the system to maintain high accuracy without excessive overall complexity.
Solution Approach 2:
The system transitions from traditional single-dimensional text matching to a multi-dimensional ranking approach by introducing both qualitative semantic evaluation and quantitative metric evaluation. This dimensional expansion enables more accurate search result ranking while managing complexity through modular component design.
2Measurement precision
If multiple scoring components are combined to improve search result relevance, then the measurement precision is improved, but the computational time and processing complexity increase
Solution Approach 1:
The system performs preliminary calculations by pre-computing quantitative scores (such as distance scores and rating scores) and storing them alongside data entries. When a search is executed, these pre-computed values are readily available for immediate combination with semantic similarity scores, eliminating the need for real-time computation of all metrics and significantly reducing processing time.
3Measurement precision
If semantic similarity scoring is prioritized over quantitative metrics, then the semantic relevance of results is improved, but the geographic and rating-based relevance may be compromised
Solution Approach 1:
The system merges the qualitative semantic similarity score with the quantitative score (encompassing distance, rating, and other location-specific metrics) into a unified ranking framework. This combination ensures that both semantic relevance and geographic/rating-based relevance are simultaneously considered, preventing the prioritization of one metric over another and maintaining comprehensive result accuracy.
Data Source
AI summary
The invention provides for a system for ranking data including a first calculation component that calculates a qualitative semantic similarity score of at least one data entry from at least one data source, a second calculation component that calculates a general quantitative score of the at least one data entry from at least one data source, a third calculation component that combines the qualitative semantic similarity score and the general quantitative score of the at least one data entry from at least one data source into a vector score for the at least one data entry, and a ranking component that ranks the at least one data entry among other data entries using the vector score. General qualitative scores such as distance scores are never overruled by semantic scores.