Interview Assistant -- AI Powered Interview Preparation and Assessment Platform

In the rapidly evolving e-commerce and digital marketplace, customers are often overwhelmed by vast product options. Traditional recommendation methods relying on static filters or manual categorization fail to capture user intent, behaviour, and preferences effectively. To address these limitations, this research introduces an AI-Driven Product Recommendation System (PRS) — an intelligent, adaptive, and context-aware platform designed to provide personalized product suggestions. The proposed system leverages Machine Learning (ML), Deep Learning, and Natural Language Processing (NLP) techniques to analyse user interactions, purchase history, and contextual data. The recommendation engine employs hybrid models integrating Collaborative Filtering (CF), Content-Based Filtering (CBF), and Neural Network architectures for enhanced accuracy. Real-time data processing enables dynamic adaptation to user behaviour, while feedback loops continuously refine model predictions. The system is implemented with a React.js frontend and Node.js/Express backend, ensuring seamless and interactive user experience. The backend integrates with MongoDB or PostgreSQL for scalable data storage, and APIs for data retrieval and product catalogue management. This research demonstrates how AI-based recommendation systems can significantly improve user engagement, conversion rates, and customer satisfaction.