Quantile Threshold Estimation for Candidate Ranking Efficiency
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Solution Overview
Problem
Existing methods for determining applicant rankings in online networks are inefficient, requiring significant computational, storage, and network overhead due to the need to calculate and transmit a large number of scores for each candidate's position in the ranking.
Innovation Solution
The system uses quantile thresholds calculated from score distributions to estimate a candidate's position in the ranking through interpolation, reducing the need for extensive score calculations and transmissions by using a limited number of fixed quantile thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to calculate and transmit scores for all candidates to determine rankings, then accurate ranking positions can be obtained, but computational overhead, storage requirements, and network transmission costs increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for ranking - specifically quantile thresholds representing score distributions - rather than transmitting all individual candidate scores. This extraction approach maintains ranking accuracy while dramatically reducing computational and network overhead by eliminating unnecessary data transmission.
Solution Approach 2:
Instead of calculating each candidate's exact ranking position by comparing against all other scores, the patent inverts the approach by pre-calculating quantile thresholds from the score distribution and using these thresholds to estimate rankings. This inversion transforms a computationally intensive many-to-many comparison problem into an efficient many-to-few threshold matching problem.
2Productivity
If quantile thresholds are used to estimate candidate positions through interpolation, then computational efficiency improves, but some precision in ranking position may be lost
Solution Approach 1:
The patent changes the parameter representation from individual candidate scores to quantile threshold values that capture the distribution characteristics. By transforming the data representation and using interpolation between quantile thresholds, the system achieves efficient estimation while maintaining acceptable precision through the mathematical properties of quantile-based distribution modeling.
3Loss of information
If all candidate scores are calculated and stored, then complete ranking information is available, but storage requirements and processing time increase
Solution Approach 1:
The patent extracts only the essential distribution characteristics (quantile thresholds) from the complete set of candidate scores, storing only these threshold values rather than all individual scores. This extraction maintains the ability to estimate any candidate's ranking position while dramatically reducing storage requirements by eliminating redundant score data.
Solution Approach 2:
The patent creates a simplified copy of the ranking information in the form of quantile thresholds that represent the score distribution. This copy contains sufficient information to estimate rankings through interpolation without requiring storage of the complete original score set, achieving space-efficient information representation.
Data Source
AI summary
The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains parameters associated with a query for a ranking of candidates for an opportunity, wherein the parameters include a candidate and the opportunity. Next, the system matches one or more of the parameters to a fixed number of quantile thresholds calculated from a distribution of scores for the candidates. The system then estimates, based on the fixed number of quantile thresholds, a quantile for a score of the candidate. Finally, the system outputs a position of the candidate within the ranking based on the estimated quantile.