Abstract

Candidates are faced with numerous challenges when seeking internship especially in IT-based firms, the challenges include elongated time-frame resulting from the conventional search of placement among others. This research presents a platform through the design of a case-based reasoning (CBR) model which mitigates the challenges and facilitates internship placements for candidates. The aim is to alleviate intern-employer mapping dilemma. The research applies supervised machine learning techniques including data pre-processing, feature extraction, document similarity metrics, and knowledge-intensive CBR pattern matching to optimize matching between intern candidate vectors and employer criteria vectors. The system resultantly introduce an ML based personalized and efficient matching platform with real-time support, potentially improving outcomes for interns and companies within the same ecosystem.

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