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All Journal INOVTEK Polbeng - Seri Informatika
Maharaya Bintangku Aridhana
Pembangunan Jaya University
Computer Science & IT

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Web-Based Job Recommendation Based on LinkedIn Profiles Using Domain-Aware SBERT Retrieval and TF-IDF Reranking Maharaya Bintangku Aridhana; Prio Handoko
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/12r31r31

Abstract

Online job search often relies on keyword matching, while the semantic relationship between candidate profiles and job descriptions may not be captured adequately. This study develops a web-based job recommendation system based on LinkedIn-style candidate profiles using SBERT retrieval and domain-aware TF-IDF reranking. Candidate profiles are constructed from target role, headline, skills, experience, education, preferred location, and work preference, with non-English input translated into English when needed. The job corpus consists of approximately 1.3 million job postings represented by precomputed 384-dimensional SBERT embeddings. The system retrieves initial candidates using cosine similarity and reranks them using TF-IDF similarity with domain, experience, and location constraints. Manual evaluation on 1,012 judged profile-job pairs shows that the proposed method achieves Precision@5 of 0.428, Precision@10 of 0.368, NDCG@10 of 0.531, and MRR of 0.605. An additional validated pseudo-label evaluation achieves Precision@5 of 0.840, with 83.33% agreement and a Cohen’s Kappa of 0.75 against human-checked samples. These results indicate that semantic retrieval combined with explainable domain-aware reranking can improve the relevance of web-based job recommendations.
Co-Authors Prio Handoko