JobMatchAI：基于知识图谱、语义搜索和可解释AI的智能岗位匹配平台
JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
- Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出JobMatchAI平台，整合Transformer嵌入、技能知识图谱和可解释重排序技术，优化技能匹配、经验、地点、薪资和公司偏好，提供 resume 驱动的搜索工作流解释，发布JobSearch-XS基准和混合检索栈评估技能泛化能力。
AI中文摘要：
招聘方和求职者依赖搜索系统导航劳动力市场，候选者匹配引擎对招聘结果至关重要。大多数系统作为关键词过滤器，无法处理技能同义词和非线性职业路径，导致候选人遗漏和匹配分数不透明。我们引入JobMatchAI，一个生产级系统，整合Transformer嵌入、技能知识图谱和可解释重排序。我们的系统优化技能匹配、经验、地点、薪资和公司偏好，通过简历驱动的搜索工作流提供因子级解释。我们发布JobSearch-XS基准和结合BM25、知识图谱和语义组件的混合检索栈以评估技能泛化能力。我们在JobSearch-XS上评估系统性能，提供演示视频、托管网站和可安装包。
英文摘要：
Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores. We introduce JobMatchAI, a production-ready system integrating Transformer embeddings, skill knowledge graphs, and interpretable reranking. Our system optimizes utility across skill fit, experience, location, salary, and company preferences, providing factor-wise explanations through resume-driven search workflows. We release JobSearch-XS benchmark and a hybrid retrieval stack combining BM25, knowledge graph and semantic components to evaluate skill generalization. We assess system performance on JobSearch-XS across retrieval tasks, provide a demo video, a hosted website and installable package.
