<![CDATA[Job recommendation systems are frequently used to help job seekers find suitable positions. Nevertheless, many existing systems focus primarily on accuracy and provide limited justification. This lack of openness can erode user confidence, particularly among recent grads who need a clear explanation of how their individual experiences fit the recommendations. Furthermore, these systems frequently lack sophisticated methods to explain the reasoning behind the recommendations, such as Retrieval-Augmented Generation (RAG), which makes them seem impersonal and difficult to trust. The purpose of this research is to develop an explainable job recommendation system that generates employment suggestions based on language comprehension by integrating RAG and Content-Based Filtering (CBF). User profiles and open positions are displayed using TF-IDF and Sentence-BERT, and then the experience level-based cosine similarity is calculated. To measure competency coverage, matching and absent skills are identified in an explicit skill-gap analysis. The Large Language Model and FAISS-based RAG modules leverage the explanations that are produced by finding matched and missing abilities as context. The CBF approach was used to evaluate recommendation relevance, while BLEU and ROUGE on ten test documents were used by HR specialists for validation. The system's mean ROUGE-1 F1 score was 0.4659, and its mean ROUGE-L score was 0.2918, based on 10 evaluation cases. Results show that the proposed recommendation system provides accurate and adequate guidelines based on HR references. This paper enriches Informatics by consolidating semantic similarity modeling, explicit competency-gap reasoning, and grounded text generation together to form a cohesive explainable recommendation framework targeted to cold-start job seekers.]]>
