AI Product Recommendation System

This project demonstrates the integration of modern AI technologies with practical e-commerce applications. The system combines natural language processing with sophisticated filtering algorithms to deliver highly personalized product recommendations.

Architecture Overview

Backend Stack

• FastAPI: High-performance API endpoints with async support
• OpenAI GPT-3.5: Core recommendation generation engine
• Python 3.8+: Modern Python features with async capabilities
• Pydantic: Robust data validation and serialization

Frontend Stack

• React 18: Modern UI with hooks for state management
• Responsive Design: Mobile-first approach with modern CSS
• Performance Optimization: Lazy loading and code splitting

Smart Recommendation Engine

• Brand Preference Handling: Weighted scoring system for brand preferences with diversity balancing
• Category Mixing: Intelligent cross-category recommendations with affinity scoring
• Performance Optimization: Achieved sub-second response times through efficient API usage

Fallback System

Implemented a robust fallback recommendation system that maintains high-quality suggestions even in offline or degraded scenarios, ensuring continuous service availability.

• Implementation of collaborative filtering algorithms
• Addition of A/B testing framework for recommendation optimization
• Enhancement of the recommendation algorithm with machine learning
• Expansion of test coverage and performance metrics

For detailed technical documentation and source code, visit the project repository:

View Project on GitHub

I. Backend (Python Ecosystem)

The backend is responsible for serving product data, handling user requests, generating recommendations, and interacting with potential AI services.

• FastAPI (Framework): A high-performance web framework for building APIs with Python. It defines the API structure, handles incoming web requests, routes them to the correct Python functions, and sends back responses.
• Pydantic (Library): A library for data validation and settings management using Python type hints. It defines the expected structure and data types for incoming requests and internal data objects.
• Uvicorn (Server): An ASGI web server implementation for Python. It runs the FastAPI application, listens for incoming network requests, and passes them to FastAPI to be processed.
• OpenAI (Library): The official Python library provided by OpenAI to interact with their AI models. It is initialized to create a client object capable of making API calls.
• Python Standard Libraries: Used for interacting with the operating system, reading data, and providing type hints.
• python-dotenv (Library): A library to load environment variables from a .env file into the application's environment.
• Backend Concepts & Methods: Includes service layer, object-oriented programming, error handling, CORS, mock/rule-based logic, and configuration management.

II. Frontend (React & JavaScript Ecosystem)

The frontend provides the user interface (UI) and interacts with the backend API.

• React (Library): A popular JavaScript library for building dynamic user interfaces based on components.
• JavaScript (Language): Provides the logic for React components, API calls, and general UI interactions.
• Fetch API (Browser API): A standard browser interface for making network requests.
• CSS (Styling): Used to define the visual appearance of the web page.

III. Data Format

• JSON (JavaScript Object Notation): A lightweight, human-readable data interchange format.

IV. General Concepts & Tooling

• API (Application Programming Interface): The contract between the frontend and backend, defining how they communicate.
• REST (Representational State Transfer): An architectural style for designing APIs.
• Environment Variables: Used in both frontend and backend to manage configuration separately from the code.
• Node.js / npm (or yarn): JavaScript runtime and package manager needed to run the React development server.
• Git: Version control system likely used to manage the codebase.