JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 15 Mar 2026.
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References · 9
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- 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 170
- 2020Declarative Experimentation in Information Retrieval using PyTerriercited by 29
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