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Kudala Sai Janya, Kuna Pavani, Shaik Umera, Manjula Vaishnavi, Rajbai Bhargavi and M Veeresha

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 798-806

Abstract: In the era of digital recruitment, students and job seekers often face information overload due to the exponential growth of online job portals. Finding relevant employment opportunities that align with specific skill sets and preferences is a significant challenge. This paper proposes a robust Recommender System for Job Postings utilizing a Hybrid Filtering approach. The system integrates Content-Based Filtering, leveraging Natural Language Processing (NLP) to analyze job descriptions and user resumes, with Collaborative Filtering, which utilizes Matrix Factorization to learn from user interaction logs. By combining these techniques, the proposed system mitigates common recommendation challenges such as the cold-start problem and data sparsity. Experimental results demonstrate that the hybrid model achieves higher accuracy and personalization compared to single-method baselines, significantly improving the job search experience.

Keywords: Recommender Systems; Hybrid Filtering; Natural Language Processing; Collaborative Filtering; Job Recruitment; Machine Learning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1668

DOI: 10.32628/IJSRST26133204

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