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.

翻译：在线招聘平台需要能够从大量且异构的职位发布集合中检索相关就业机会的推荐方法。关键词搜索高效且可解释，但当相同岗位使用不同术语表述时，可能无法检索到相关发布。本研究提出了一种元数据驱动的职位推荐系统，该系统融合了TF-IDF词汇匹配、Sentence-BERT语义检索、查询感知过滤、可选的Cross-Encoder重排序以及解释生成。所提出的系统利用结构化元数据字段（包括职位名称、公司名称、地点、资历级别、工作职能、雇佣类型和行业），无需依赖完整的职位描述或用户交互历史。在包含31,262条记录的清洗后的LinkedIn职位发布数据集上进行的实验表明，最佳混合配置实现了0.8032的前十位精确率（Precision at 10）和0.9496的前十位归一化折损累计增益（nDCG at 10）。在内部评估协议下，Cross-Encoder重排序将前十位精确率从0.7896提升至0.7948，前十位nDCG从0.9666提升至0.9739。这些发现表明，在仅提供结构化元数据的情况下，可以有效地结合词汇检索与语义检索技术，以提供可解释的职位推荐。