Ads Recommendation Service

Introduction

The Ads Recommendation Service is a machine learning-powered system designed to provide personalized ad recommendations to users based on their interactions and preferences. Built using FastAPI, SQLAlchemy, and PyTorch, this service leverages user activity data to train a recommendation model that predicts which ads a user is likely to engage with.

Features

• User registration and management
• Ad creation and management
• User activity tracking
• Machine learning model for ad recommendations
• Background training of the recommendation model
• RESTful API for easy integration

File and Folder Structure

.
├── app
│   ├── api
│   │   ├── v1
│   │   │   ├── endpoints
│   │   │   │   ├── ads.py
│   │   │   │   ├── training.py
│   │   │   │   └── users.py
│   │   │   ├── models.py
│   │   └── __init__.py
│   ├── config.py
│   ├── core
│   │   ├── recommendation
│   │   │   ├── features.py
│   │   │   ├── model.py
│   │   │   └── training.py
│   │   └── __init__.py
│   ├── db
│   │   ├── models
│   │   │   ├── ads.py
│   │   │   ├── users.py
│   │   │   └── user_activities.py
│   │   ├── session.py
│   │   └── __init__.py
│   ├── services
│   │   ├── ads_service.py
│   │   └── user_service.py
│   ├── core
│   │   ├── schemas
│   │   │   ├── ads.py
│   │   │   └── users.py
│   │   └── __init__.py
│   ├── main.py
│   └── __init__.py
├── tests
│   ├── test_api
│   │   ├── test_endpoints
│   │   │   ├── test_ads.py
│   │   │   └── test_users.py
│   │   └── conftest.py
│   └── __init__.py
├── .env.example
├── .gitignore
├── docker-compose.yml
├── Dockerfile
└── requirements.txt

File Descriptions

• app/: The main application directory containing all the source code.

  • api/: Contains the API endpoints for the application.
    • v1/: Version 1 of the API.
      • endpoints/: Contains the FastAPI route handlers.
        • ads.py: Handles ad-related operations (creation, recommendations).
        • training.py: Manages model training operations.
        • users.py: Manages user-related operations (registration, preferences).
      • models.py: Contains Pydantic models for API responses and requests.
  • config.py: Configuration settings for the application, including environment variables.
  • core/: Contains core functionalities of the application.
    • recommendation/: Contains the recommendation model and training logic.
      • features.py: Extracts features from user activities for training.
      • model.py: Defines the recommendation model architecture.
      • training.py: Contains the logic for training the recommendation model.
  • db/: Database-related files.
    • models/: Contains SQLAlchemy models for ads, users, and user activities.
    • session.py: Manages database sessions and connections.
  • services/: Contains service classes that encapsulate business logic.
    • ads_service.py: Handles ad-related business logic.
    • user_service.py: Handles user-related business logic.
  • core/schemas/: Contains Pydantic schemas for data validation and serialization.
  • main.py: The entry point of the application, where the FastAPI app is created and configured.

• tests/: Contains unit and integration tests for the application.

  • test_api/: Contains tests for the API endpoints.
    • test_endpoints/: Contains individual test files for ads and users.
    • conftest.py: Contains fixtures and setup code for tests.

• .env.example: Example environment variables file for configuration.

• .gitignore: Specifies files and directories to be ignored by Git.

• docker-compose.yml: Docker Compose configuration for running the application and database.

• Dockerfile: Dockerfile for building the application image.

• requirements.txt: Lists the Python dependencies required for the application.

Getting Started

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

4. Set up the environment variables by copying .env.example to .env and updating the values.

5. Run the application:

   uvicorn app.main:app --host 0.0.0.0 --port 8000

6. Access the API at http://localhost:8000/api/v1/.

Running Tests

To run the tests, use the following command:

pytest

License

This project is licensed under the MIT License - see the LICENSE file for details.