Evaluator Matching by Functional Ranking and Constraint Filtering
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Solution Overview
Problem
Current systems face challenges in efficiently matching data records for evaluation purposes, particularly in identifying suitable evaluators for ratees, due to computational complexity, dynamic data changes, and constraints, leading to suboptimal feedback and unrepresentative evaluations.
Innovation Solution
A computational match system that classifies candidate data objects into functional categories, generates ranked listings based on interaction scores, applies constraints, and automatically recommends evaluator groups for evaluation requests, allowing for semi-automated adjustments and user input to refine the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a computational match system classifies candidate data objects into functional categories and generates ranked listings based on interaction scores to improve evaluator recommendation accuracy, then the matching precision is improved, but the computational complexity increases
Solution Approach 1:
The system segments the candidate evaluator pool into multiple functional categories (e.g., peer evaluators, managerial evaluators, cross-functional evaluators) based on their organizational roles and relationships. This segmentation allows the system to process and evaluate candidates in manageable groups rather than as a single large set, improving matching precision while controlling computational complexity through structured classification.
Solution Approach 2:
The system changes evaluation parameters by assigning different weights and criteria to different functional categories. Each category can be evaluated using category-specific parameters (e.g., peer proximity for peer evaluators, performance knowledge for managerial evaluators), allowing precise matching without uniformly complex computation across all candidates.
2Reliability
If the system applies multiple constraints to limit the number of candidate evaluators to ensure diverse and meaningful feedback, then the evaluation quality is improved, but the selection process becomes more complex
Solution Approach 1:
The system dynamically adjusts constraint application based on the evaluation context and category. Different functional categories can have different constraint levels applied (e.g., minimum number of peer evaluators, maximum managerial hierarchy depth), allowing the selection process to adapt to specific evaluation needs while maintaining overall quality standards without rigid uniform complexity.
Solution Approach 2:
The system performs preliminary filtering and categorization of candidate evaluators before applying detailed constraint evaluation. By pre-classifying candidates into functional categories and pre-assessing basic eligibility criteria, the system reduces the complexity of subsequent constraint application while ensuring evaluation quality through structured preliminary screening.
3Productivity
If the system generates ranked listings for each functional category and automatically recommends evaluator groups, then the productivity of evaluation request processing is improved, but the extent of automation increases system complexity
Solution Approach 1:
The system implements self-service automation where the computational match system automatically generates ranked listings and recommends evaluator groups without requiring manual intervention. The system serves itself by autonomously processing evaluation requests, applying constraints, and producing final recommendations, thereby improving productivity while managing automation complexity through self-contained processing logic.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated recommendation process learns from evaluation outcomes and user adjustments. By analyzing which automated recommendations are accepted or modified, the system refines its matching algorithms and constraint application, improving productivity over time while managing automation complexity through adaptive learning rather than rigid fixed rules.
4Adaptability or versatility
If the system allows semi-automated adjustments and user input to refine evaluator selection, then the adaptability is improved, but the ease of operation decreases
Solution Approach 1:
The system applies local quality by allowing user adjustments specifically in areas where adaptability is needed (e.g., modifying evaluator categories, adjusting constraint parameters for specific evaluations) while maintaining automated processing for standard cases. Users can refine selections locally without reconfiguring the entire system, improving adaptability while preserving ease of operation through targeted rather than global intervention.
Data Source
AI summary
Systems and methods obtain an interaction listing for a target individual, in which the interaction listing includes potential evaluators for the target individual, in which each potential evaluator is associated with a score representing a volume of interactions between the potential evaluator and the target individual over a period of time; obtain a functional category designation for each potential evaluator; assign each potential evaluator to an applicable category based on the designation; for each category: create a ranked listing of potential evaluators by ordering the potential evaluators based on their associated scores; and identify a threshold number of potential evaluators from the list; for each identified potential evaluator: determine whether the identified potential evaluator satisfies one or more evaluation constraints; and if so, add the potential evaluator to an evaluation group for the target individual; generate an evaluation request; and provide the request to each potential evaluator within the evaluation group.