GESA提升求职匹配精准度与公平性，支持快速解释决策。
GESA: Graph-Enhanced Semantic Allocation for Generalized, Fair, and Explainable Candidate-Role Matching
- 用图神经网络融合候选者与职位的语义关系，动态建模复杂匹配
- 在2万份简历、3000个职位上实现94.5%精准率，多样性提升37%
- 内置去偏机制与可解释模块，适合跨国企业、高校等场景
准确、公平且可解释的候选人与岗位匹配是企业招聘、学术招生、资助评选及志愿分配等领域的重要挑战。现有先进方法存在语义僵化、持续性人口偏差、决策过程不透明以及在动态政策下可扩展性差等问题。本文提出GESA（Graph-Enhanced Semantic Allocation）框架，通过整合领域自适应的Transformer嵌入、异构自监督图神经网络、对抗去偏机制、多目标遗传优化和可解释AI组件，系统性解决上述问题。在包含20,000名候选人和3,000个职位规格的大规模国际基准上评估，GESA实现94.5%的前3名匹配准确率，多样性表现提升37%，跨人群公平性得分为0.98，端到端延迟低于1秒。此外，GESA支持混合推荐与玻璃盒式可解释性，适用于工业界、学术界及非营利组织的多样化国际部署。
原文摘要 · Abstract (English)
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 categories, 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.
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