Abstract:

In the digital age we live in now, personalized recommendation systems are a key part of e commerce sites. They greatly improve user engagement and satisfaction. This paper describes how to design and build a Personalized Shopping Assistant, an intelligent web-based platform that suggests products based on each user's preferences, interactions, and behavior. The suggested system uses modern web technologies and content-based filtering to give each user accurate and understandable recommendations that are specific to their shopping journey.The system architecture consists of a React based frontend that provides dynamic and interactive user experiences, a Node.js and Express-powered backend that handles the business logic, and a MongoDB database that stores and retrieves user and product data quickly and easily. To find out how close user Management Simulator, created with Python and Flask for the backend and HTML/CSS/JS for the frontend. The simulator takes input from the user, runs the algorithms they choose, and shows memory states, page hits, and faults in real time on a graph. The system also tells you which algorithm works best based on metrics like the Average Memory