用知识图谱和语义搜索提升招聘匹配精准度，还能解释推荐理由。
JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
- 融合技能知识图谱与嵌入模型，理解技能同义词和非线性职业路径。
- 在新基准上实现92.3%的召回率，比传统方法提升18.7个百分点。
- 适合招聘系统开发者、求职平台优化者及需要可解释推荐的团队。
招聘人员与求职者依赖搜索系统来应对劳动力市场，因此候选人匹配引擎对招聘结果至关重要。现有系统多为关键词过滤器，无法处理技能同义词和非线性职业路径，导致错失潜在人选且匹配评分不透明。我们提出JobMatchAI，一个可投入生产的匹配平台，整合Transformer嵌入、技能知识图谱与可解释重排序技术。系统在技能匹配度、经验、地点、薪资及公司偏好等多个维度优化推荐效果，并通过简历驱动的搜索流程提供各因素的解释。我们发布了JobSearch-XS基准数据集，以及结合BM25、知识图谱与语义组件的混合检索架构，用于评估技能泛化能力。在JobSearch-XS上评估了系统在多种检索任务中的表现，提供了演示视频、在线网站及可安装包。
原文摘要 · Abstract (English)
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.
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