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@article{190839,
        author = {Pooja Athare and Sonam Vavare and Asmita Bagal and Om Gaikwad},
        title = {Skill-Based Hiring Systems Using Intelligent Matching Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5250-5252},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190839},
        abstract = {Traditional recruitment systems rely heavily on academic qualifications and keyword-based resume filtering, which often fail to recognize practical skills and real-world competencies of candidates. This limitation results in biased hiring decisions and inefficiencies in talent acquisition. Skill-based hiring systems aim to overcome these challenges by focusing on candidate skills, experience, and job requirements rather than formal degrees alone. This survey paper reviews existing skill-based hiring and job recommendation systems, highlighting intelligent matching techniques such as cosine similarity, machine learning, and recommendation algorithms. The paper analyzes various methodologies, compares their performance, identifies research gaps, and discusses challenges in deploying scalable and inclusive hiring platforms. Based on the study, future research directions are outlined to enhance accuracy, fairness, and automation in recruitment systems.},
        keywords = {Skill-Based Hiring, Job Recommendation System, Cosine Similarity, Recruitment Automation, Intelligent Matching.},
        month = {January},
        }