Abstract:
The recruitment process is a critical and complex aspect of organizational development, requiring significant time and resources to identify suitable candidates for various roles. Traditional interview methods often lack efficiency, consistency, and scalability, which can result in suboptimal hiring decisions. To address these challenges, this project introduces E-Interviewer, an AI-driven interview analytics platform designed to transform the hiring process through automation, data-driven insights, and advanced artificial intelligence capabilities. E-Interviewer integrates multiple functionalities to assist both recruiters and candidates. The platform leverages Natural Language Processing (NLP) models for resume parsing, extracting key details such as names, contact information, and skills, while matching candidate profiles with job descriptions. Skill extraction and matching are further enhanced Named Entity Recognition (NER) models and similarity analysis techniques. In addition, the system incorporates a personality prediction model, which analyzes candidate responses to determine traits such as extroversion, openness, and conscientiousness, aiding recruiters in making more informed decisions. The platform supports asynchronous interview, pre recorded video-based question-andanswer sessions where candidates respond to predefined questions at scheduled time and under given time. Recruiters on the other hand can check the responses at their convenience where AI emotion analytics are performed to check the active state emotion and overall behavior in specific video response of candidate. In addition, this platform supports supports personality assessment and role specific written test with role specific questions, the system utilizes generative AI models to assist recruiters in designing these role-specific questions based on required skills. To ensure a seamless user experience, E-Interviewer features an intuitive user interface developed using React.js, and backend using Flask. The system employs MySQL for database, storing and retrieving user data efficiently. Additional features include candidate ranking, automated test scheduling, and detailed performance analytics, making it a comprehensive solution for modern recruitment challenges. E-Interviewer not only reduces the time and cost of the hiring process but also promotes fairness and objectivity by minimizing human biases. Its AI-driven approach enhances the accuracy and reliability of candidate assessments, ultimately helping organizations build stronger, more capable teams. This project serves as a step toward revolutionizing the recruitment landscape, leveraging technology to bring innovation, efficiency, and transparency to the forefront of human resource management.
