Developing an Intelligent Job Recommendation System Using Semantic Retrieval and Explainable AI Techniques 文章

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 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.