Anime Recommendation System (Honours Project)

A system for recommending anime focused on improving recommendations for new users by creating a hybrid recommendation system. It uses MyAnimeList for the user ratings data and for the anime data.

Running the Project

This project requires Python. Python 3.10 is recommended since there is an issue with the way the Surprise package is setup. There is a file called requirements.txt which can be used to install all the required Python dependencies.

Getting the Required Datasets

Downloading the Data Files

Downloaded files should go inside the data folder.

You can download the data from here: https://drive.google.com/drive/folders/1byfM21Q65Mn5gb2SVcutu_z-MspwJbr3, or you could download the appropriate files using apiDataCollector.py and jsonConverter.py. You also need to download the rating_complete.csv file from the Kaggle Dataset at: https://drive.google.com/drive/u/1/folders/1VHbxxhSLdK_g7ro7-3a7ntgL1YXdp-WT.

Getting the Data Yourself (Optional)

If you want, you can manually collect MyAnimeList data using the python files provided (apiDataCollector.py and jsonConverter.py). You will need a .env file with your MyAnimeList API Client ID:

CLIENT_ID=<MY ANIME LIST API CLIENT ID>

Using the Streamlit Web App

Run the following commands to start the web app:

cd web-app
streamlit run main_page.py

You will need to run the RecommendationSystems Jupyter Notebook at least once so the relevant files are saved to the data folder before running the streamlit app.