JobMatchAI An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
This addresses the need for more effective and transparent job matching for recruiters and job seekers, though it appears incremental as it builds on existing components like knowledge graphs and semantic search.
The paper tackles the problem of job matching systems being limited to keyword filters, which miss candidates and lack transparency, by introducing JobMatchAI, a production-ready platform that integrates Transformer embeddings, skill knowledge graphs, and interpretable reranking to optimize utility across multiple factors and provide explanations.
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.