Determining expert relevance with respect to interview board subject and candidates

The Interview Matching System is an AI-driven web application designed to automate and optimize the process of aligning candidates with suitable interviewers based on skill relevance and domain expertise. Conventional recruitment workflows depend heavily on manual resume screening, subjective decision-making, and time-consuming coordination. To address these challenges, the proposed system integrates artificial intelligence, natural language processing, and semantic similarity analysis to evaluate candidate resumes and generate accurate interviewer–candidate matches. The platform is developed using the Flask framework with SQL Alchemy ORM and offers role-based access for candidates, interviewers, and administrators through dedicated dashboards. Candidate resumes undergo text extraction and are analysed using the Gemini AI model or a fallback keyword based mechanism to identify key skills and attributes. A pre-trained Doc2Vec model is employed to compute similarity scores between candidate profiles and interviewer expertise, enabling intelligent and automated match recommendations. Administrators can schedule interviews based on availability and compatibility, while the system also provides feedback management and analytics to monitor performance. By automating decision making and enhancing data-driven evaluation, the Interview Matching System reduces recruitment time, minimizes human bias, and improves the overall efficiency of interview management.