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

Researchers developed an intelligent job recommendation system combining TF-IDF lexical matching with Sentence-BERT semantic retrieval to improve job posting searches on recruitment platforms. The hybrid approach achieved strong performance metrics (Precision@10: 0.8032, nDCG@10: 0.9496) using only structured metadata fields, demonstrating that semantic and lexical techniques can effectively complement each other for explainable recommendations.

This research addresses a fundamental challenge in online recruitment: bridging the gap between keyword-based search efficiency and semantic understanding of job market terminology. Traditional keyword matching fails when similar roles use different terminology, limiting candidate-job matching quality. The proposed system tackles this by combining lexical and semantic approaches, leveraging Sentence-BERT embeddings alongside TF-IDF to understand contextual meaning rather than exact word matches.

The work builds on broader trends in AI-driven recruitment technology, where platforms increasingly adopt neural retrieval methods to improve user experience. Unlike many modern recommendation systems that require extensive user interaction history or full job descriptions, this approach operates efficiently with only structured metadata—job title, company, location, seniority, function, employment type, and industry. This constraint reflects practical deployment realities where complete data may be unavailable or computationally expensive to process.

The market implications are significant for recruitment technology vendors and job platforms serving millions of users. Improved semantic matching directly translates to better user engagement, reduced hiring friction, and higher platform value. The Cross-Encoder re-ranking component shows incremental gains, suggesting that layered architectural approaches offer measurable performance improvements. For developers, the research demonstrates that effective AI solutions don't require massive datasets or complex user behavior modeling—structured metadata combined with modern NLP techniques delivers production-grade results.

Looking ahead, the field will likely see increased adoption of hybrid retrieval architectures in recruitment platforms, with explainability becoming a competitive differentiator as users demand transparency in algorithmic decisions.

• →Hybrid lexical-semantic retrieval outperforms single-method approaches for job recommendation accuracy
• →Structured metadata alone enables strong recommendation performance without full descriptions or user history
• →Cross-Encoder re-ranking provides measurable performance improvements in precision and ranking quality
• →Semantic embeddings address terminology inconsistency problems inherent in keyword-based job matching
• →Explainable AI techniques are feasible in production recruitment systems with standard NLP architectures