Developing an Intelligent Job Recommendation System Using Semantic Retrieval and Explainable AI Techniques
arXiv:2605.27656v1 Announce Type: cross Abstract: Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections of job postings. Keyword-based search is efficient and interpretable, but it may fail to retrieve relevant postings when equivalent roles are expressed using different terminology. This study presents a metadata-driven job recommendation system that combines TF-IDF lexical matching, Sentence-BERT semantic retrieval, query-aware filtering, optional Cross-Encoder re-ranking, and explanation generati
The proliferation of online recruitment platforms and the increasing complexity of job markets necessitate more advanced, intelligent recommendation systems to connect talent with opportunity effectively, moving beyond simple keyword matching.
This development indicates a significant advancement in leveraging AI for critical white-collar workflows like recruitment, enhancing efficiency and accuracy in talent acquisition, and paving the way for more sophisticated agentic systems.
Job recommendation systems are evolving from keyword-centric to semantically aware and explainable, offering more relevant matches and transparency to users.
- · Online recruitment platforms
- · Job seekers
- · Employers
- · AI/ML developers
- · Traditional keyword-based search systems
- · Recruiters reliant solely on manual matching
Improved matching efficiency in online recruitment leading to better employment outcomes.
Increased adoption of explainable AI in other adjacent HR tech and professional services sectors.
The development of fully autonomous AI agents capable of entire end-to-end recruitment pipelines, from sourcing to initial interviews.
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Read at arXiv cs.AI