Product Recommendation

Overview

This project is a Product Recommendation System built using Spring Boot and H2 Database. It tracks user interactions such as views, purchases, and ratings to recommend popular products.

Features Implemented

• User Activity Tracking: Captures product views, purchases, and ratings through API endpoints.
• Popular Product Recommendation: Returns a list of frequently viewed or purchased products.
• Spring Boot REST API: Provides seamless integration with e-commerce frontends.
• H2 In-Memory Database: Used for data storage and quick retrieval of recommendations.

Endpoints

Tracking User Activity

• POST /track/view?userId={userId}&productId={productId} → Track when a user views a product.
• POST /track/purchase?userId={userId}&productId={productId} → Track when a user purchases a product.
• POST /track/rate?productId={productId}&rating={rating} → Record user ratings for a product.

Getting Recommendations

• GET /recommend/popular → Fetches the most viewed or purchased products.

Tech Stack

• Backend: Spring Boot
• Database: H2 (In-memory)
• API Documentation: Postman

To-Do

• Implement collaborative filtering to recommend products frequently viewed or purchased together.
• Optimize query performance using caching (e.g., Redis).
• Store user activity data in MongoDB instead of H2 for scalability.
• Improve recommendation accuracy by integrating machine learning models.

How to Run

1. Clone this repository: https://github.com/Aryahi/ProductRecommendation.git
2. Navigate to the project directory:cd ProductRecommendation
3. Run the Spring Boot application:mvn spring-boot:run
4. Access the APIs using Postman or any API testing tool.

Future Enhancements

• Personalization based on user preferences and browsing history.
• Integration with React frontend for a full-stack solution.
• Performance improvements for real-time recommendations.

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