Confidence-Based Ranking Algorithm for Search Result Ordering
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Solution Overview
Problem
Existing database search systems face challenges in accurately retrieving useful data due to the vast amount of available data, often omitting relevant documents or including irrelevant ones, as they rely on term frequency, which fails to distinguish between useful and useless data effectively.
Innovation Solution
A confidence-based ranking algorithm that evaluates the accuracy of attribute values in data records, assigns confidence values, and stores them alongside the data, creating a searchable data structure like an inverted index, allowing for a more inclusive and accurate search results list by ordering records based on confidence values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If term frequency is used to assess document usefulness, then search speed is improved, but measurement precision deteriorates because it omits certain relevant documents and cannot distinguish between useful and useless data
Solution Approach 1:
The patent changes the assessment parameter from term frequency to confidence values. Instead of counting how often terms appear, the system evaluates the reliability and accuracy of attribute values in data records, assigning confidence scores that reflect the likelihood of data correctness. This parameter change enables both comprehensive document inclusion and accurate usefulness assessment.
2Measurement precision
If an over-inclusive search engine includes all possible data, then measurement precision is improved by not omitting relevant documents, but loss of information worsens because useless data overwhelms useful data
Solution Approach 1:
The patent applies local quality by differentiating the value of individual data records through confidence scores. Instead of treating all documents uniformly, the system assigns different confidence levels to different records based on their attribute accuracy. This allows the search engine to include all potential matches while weighting them according to their reliability, ensuring useful data stands out from useless data.
3Measurement precision
If confidence values are calculated for all attribute values in all data records, then measurement precision is improved, but use of energy and device complexity worsen due to the computational overhead
Solution Approach 1:
The patent implements partial action by calculating confidence values selectively rather than uniformly for all attributes and records. The system focuses confidence calculation on critical attributes that most impact search relevance and on records that match search criteria. This partial approach maintains measurement precision for decision-making attributes while reducing overall computational energy consumption.
Data Source
AI summary
A method for using a confidence based ranking algorithm is described. At least one search parameter is received. The at least one search parameter is used to identify at least one data record with confidence values. A results list with one or more data records is created. The results list is ordered according to the confidence values within the data records. The results list is sent.