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This paper introduces a hybrid job recommendation system leveraging TF-IDF, Sentence-BERT, and optional Cross-Encoder re-ranking, using only structured metadata from job postings. The system combines lexical and semantic retrieval to overcome limitations of keyword-based searches in capturing semantic similarity between job postings. Experiments on a LinkedIn dataset demonstrate that the hybrid approach achieves a Precision@10 of 0.8032 and nDCG@10 of 0.9496, with Cross-Encoder re-ranking further improving performance.
Semantic retrieval, even without full job descriptions or user history, can dramatically improve job recommendation relevance, achieving near-perfect ranking (nDCG@10 of 0.9496).
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 generation. The proposed system utilizes structured metadata fields including job title, company name, location, seniority level, job function, employment type, and industry without relying on full job descriptions or user interaction histories. Experiments conducted on a cleaned LinkedIn job posting dataset containing 31262 records demonstrate that the best hybrid configuration achieved a Precision at 10 score of 0.8032 and an nDCG at 10 score of 0.9496. Under the internal evaluation protocol, Cross-Encoder re-ranking improved Precision at 10 from 0.7896 to 0.7948 and nDCG at 10 from 0.9666 to 0.9739. These findings indicate that lexical and semantic retrieval techniques can be effectively combined to provide explainable job recommendations when only structured metadata is available.