GESA: Graph-Enhanced Semantic Allocation for Generalized, Fair, and Explainable Candidate-Role Matching

2025-09-29

AI Summary

Candidate-role matching in recruitment, admissions, and volunteer allocation faces challenges including inadequate semantic modeling, persistent demographic bias, opaque decision-making, and poor scalability under dynamic constraints. This paper proposes an end-to-end interpretable multi-objective optimization framework that innovatively integrates domain-adaptive Transformer embeddings, heterogeneous graph self-supervised learning, and adversarial debiasing to construct a “glass-box” matching model. The method supports multi-dimensional fairness constraints, diversity preservation, and real-time policy adaptation. Evaluated on a large-scale international benchmark, it achieves 94.5% Top-3 accuracy, a 37% improvement in diversity, a fairness score of 0.98 (Δ=0.12), and end-to-end latency under 1 second—significantly outperforming existing state-of-the-art approaches.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Learning Preferences or Rankings
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Accurate, fair, and explainable allocation of candidates to roles represents a fundamental challenge across multiple domains including corporate hiring, academic admissions, fellowship awards, and volunteer placement systems. Current state-of-the-art approaches suffer from semantic inflexibility, persistent demographic bias, opacity in decision-making processes, and poor scalability under dynamic policy constraints. We present GESA (Graph-Enhanced Semantic Allocation), a comprehensive framework that addresses these limitations through the integration of domain-adaptive transformer embeddings, heterogeneous self-supervised graph neural networks, adversarial debiasing mechanisms, multi-objective genetic optimization, and explainable AI components. Our experimental evaluation on large-scale international benchmarks comprising 20,000 candidate profiles and 3,000 role specifications demonstrates superior performance with 94.5% top-3 allocation accuracy, 37% improvement in diversity representation, 0.98 fairness score across demographic cate- gories, and sub-second end-to-end latency. Additionally, GESA incorporates hybrid recommendation capabilities and glass-box explainability, making it suitable for deployment across diverse international contexts in industry, academia, and non-profit sectors.

Problem

• Addresses semantic inflexibility in candidate-role matching systems
• Combats demographic bias and opacity in allocation decisions
• Improves scalability under dynamic policy constraints

Innovation

• Domain-adaptive transformer embeddings enhance semantic matching
• Heterogeneous graph neural networks enable self-supervised learning
• Adversarial debiasing and genetic optimization ensure fairness