Anime Recommendation System

Introduction

The Anime Recommendation System helps users discover new anime based on preferences using content-based filtering. It provides personalized recommendations through a FastAPI-based web service, allowing users to request anime suggestions based on genres, ratings, and other criteria.

Features

• Fetch anime recommendations via API.
• Uses content-based filtering for recommendations.
• FastAPI backend with interactive Swagger UI.
• Supports filtering by genres, ratings, and other factors.
• Processes anime datasets for meaningful insights.

Prerequisites

Ensure you have installed:

• Python 3.8+
• FastAPI
• Uvicorn
• Pandas, Numpy, Scikit-learn
• Matplotlib, Seaborn, Plotly (for visualization)
• Jupyter Notebook (for experimentation)

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/anime-recommendation-system.git
   cd anime-recommendation-system

2. Create a virtual environment:

   python -m venv env
   source env/bin/activate  # Windows: env\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Open Jupyter Notebook and run:

   jupyter notebook

5. Execute anime_recommendation.ipynb to generate recommendations.

Running the API

Start the FastAPI server:

uvicorn api:app --reload

Access the interactive API documentation at:

http://127.0.0.1:8080/docs

Example API Request

GET http://127.0.0.1:8080/recommend/?anime_name=Hunter%20x%20Hunter&criterion=genres

Dataset Information

The system works with datasets containing:

• Anime titles
• Genres
• User ratings
• Popularity scores

Methodology

• Content-Based Filtering: Uses metadata like genres and descriptions to find similar anime.

Troubleshooting

• 500 Internal Server Error: Check api.py and dataset loading.
• Connection Refused: Ensure Uvicorn is running on the correct port.
• Module Not Found: Verify dependencies and correct working directory.

Future Enhancements

• Integrate deep learning for better recommendations.
• Develop a web-based UI.
• Expand dataset sources for improved accuracy.
• Implement real-time updates based on user feedback.
• Use sentiment analysis from user reviews.

Contribution

Feel free to fork the repository, make improvements, and submit a pull request.

License

MIT License. Free to use and modify.