The project aims to develop an AI-powered REST API that delivers personalized product recommendations to users based on their browsing history, preferences, and behavior. It enhances user experience, boosts engagement, and helps e-commerce platforms provide targeted suggestions efficiently and accurately.
Analyze user behavior, browsing history, and purchase patterns to define recommendation strategies.
Design and implement a REST API to serve personalized product recommendations.
Integrate machine learning algorithms (e.g., collaborative filtering, content-based filtering) for accurate predictions.
Preprocess and clean user and product data to improve recommendation quality.
Develop authentication and secure API access mechanisms for clients and applications.
Create endpoints for retrieving recommended products, filtering by categories, and updating user preferences.
Test API responses for accuracy, performance, and scalability under different loads.
Implement logging and monitoring to track API usage and detect potential issues.
Deploy the API on a cloud or server environment and integrate it with a front-end or e-commerce platform for demonstration.
