Database Search Diversity Enhancement via Iterative Scoring Operator Adjustment
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Solution Overview
Problem
Current database search systems face challenges in enhancing diversity in search results while maintaining a sufficient level of match, particularly in hiring contexts, where they often prioritize diversity statistics over candidate qualifications, leading to less qualified hires.
Innovation Solution
A method and system for database search enhancement that calculates a diversity measure based on class labels, adjusts the search query by analyzing reference sets of records, and iteratively refines the scoring computational operator to improve diversity without compromising match quality, using techniques like quorum-based and contrasting population-based optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If diversity measures are used to enhance search results, then diversity in search results is improved, but match quality between search query and records deteriorates
Solution Approach 1:
The system dynamically adjusts the scoring computational operator parameters based on diversity measures. When diversity is insufficient, the system modifies the scoring operator to re-evaluate records, creating a feedback loop that adapts the matching criteria to improve diversity while maintaining match quality through iterative parameter optimization
Solution Approach 2:
The system calculates diversity measures from search results and uses this feedback to determine whether to modify the scoring computational operator. This closed-loop feedback mechanism allows the system to automatically adjust search criteria based on the diversity of returned results, resolving the contradiction between improving diversity and maintaining match precision
2Adaptability or versatility
If the scoring computational operator is modified to improve diversity, then diversity measure is improved, but search query complexity increases
Solution Approach 1:
The system automatically modifies the scoring computational operator based on diversity measures without requiring manual intervention. The process self-regulates by calculating diversity, comparing it against thresholds, and automatically adjusting the scoring operator accordingly, thereby improving diversity while avoiding the complexity of manual query modification
Solution Approach 2:
The system modifies parameters of the scoring computational operator in a controlled manner based on diversity analysis. By systematically adjusting operator parameters rather than rewriting entire queries, the system improves diversity while managing complexity through parameter-level optimization rather than structural changes
Data Source
AI summary
A method, system and computer program product for database search enhancement and interactive user interface therefor. Database records are ranked by a match score, calculated using a plurality of criteria for determining a match level between values of a record and a search query, and using a plurality of priority parameters for aggregating the match level determined. A plurality of top-ranking records is selected, and a diversity measure is calculated therefor, using at least one class label assigned to records therein. If a sufficiency condition is not met by the diversity measure, at least one reference set of records sharing a class label in common is extracted from the plurality of top-ranking records and analyzed for determining at least one modification to the search query in improvement of the diversity measure, the scoring computational operator is accordingly redefined, and the process reiterates; otherwise, the plurality of top-ranking records is outputted.