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This slide presents an overview of the proposed system architecture.
First, the system follows a resume-centric candidate profiling approach, where candidate resumes are used as the primary input to capture educational background, skill sets, and professional experience.
Next, automated information extraction is applied to systematically extract relevant attributes such as degrees, skills, and work experience, forming structured candidate profiles.
These extracted attributes are then transformed into machine-readable features through feature representation and analysis, making them suitable for downstream machine learning models.
Based on these features, the system performs learning-based job matching by identifying compatibility patterns between candidate profiles and job requirements.
Finally, the system generates a personalized job recommendation output in the form of a ranked list of job positions, prioritizing roles with the highest predicted suitability for each candidate. |
IEEE/ICACT20260013 Slide.06
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