Document Ranking Verification via Score Misalignment Detection
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Solution Overview
Problem
Current machine learning algorithms face challenges in detecting errors in document ranking, which hinders the improvement of ranking accuracy and requires extensive computational and monetary resources for training data labeling.
Innovation Solution
A method and system for selecting potentially erroneously ranked documents by computing verification scores based on differences in relevance scores and feature vectors, identifying pairs with extreme verification scores indicative of misalignment, and marking them for verification by a search engine server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning algorithms are used for document ranking, then ranking automation is improved, but error detection capability deteriorates
Solution Approach 1:
The patent introduces an intermediary verification system that computes verification scores based on feature vector differences and relevance score differences. This intermediary layer detects potential ranking errors without requiring full manual review, thus maintaining automation while improving error detection capability.
Solution Approach 2:
The system implements feedback by computing verification scores for document pairs and using extreme verification scores to identify potentially erroneously ranked documents. This feedback mechanism allows the system to self-correct and improve ranking accuracy while maintaining automation.
2Measurement precision
If extensive training data labeling is performed, then ranking accuracy is improved, but computational resources and time are increased
Solution Approach 1:
The patent extracts only the most critical document pairs for verification by computing verification scores and selecting those with extreme values. This selective approach focuses computational resources on the most problematic cases rather than processing all training data, thus reducing time and resource consumption while maintaining accuracy improvement.
Solution Approach 2:
The system performs partial verification by computing verification scores for document pairs and selectively verifying only those with extreme scores. This partial action approach avoids the need for extensive full-scale verification while still achieving accuracy improvement through targeted correction of erroneous rankings.
3Measurement precision
If verification scores are computed for all document pairs, then error detection is improved, but computational complexity is increased
Solution Approach 1:
The patent computes verification scores for document pairs but selectively processes only those with extreme verification scores. This partial action approach maintains high error detection precision by focusing on the most suspicious cases while avoiding the computational burden of analyzing all document pairs exhaustively.
Solution Approach 2:
The system applies different levels of verification intensity to different document pairs based on their verification scores. Document pairs with extreme scores receive detailed verification and potential correction, while other pairs receive minimal or no verification. This local quality approach optimizes computational complexity by concentrating resources where they are most needed.
Data Source
AI summary
A method and a system for selecting a potentially erroneously ranked document in a set of search results responsive to a query comprising receiving the set of search results from the search engine server, each document of the set of search results having a relevance score and a feature vector generated by an MLA, computing for each possible pair of documents a first parameter indicative of a level of difference in the relevance scores of the documents of the pair of documents and a second parameter indicative of a level of difference in the feature vectors of the documents of the pair of documents, computing a verification score based on first parameter and the second parameter, the verification score indicative of a level of misalignment between the relevance scores and the feature vectors, selecting and marking the pair of documents associated with an extreme verification score for verification.