Kelbrum
An anime recommendation system designed to recommend anime that is similar to user-selected anime.
Technologies Used
JavaScriptNode.jsReactReact RouterTailwindCSSDaisyUITensorflow.js
Timeline
Feb 2024 - Mar 2024Co-Authors
This project was completed individuallyKey Accomplishments
- Leveraged Tensorflow.js and additional npm packages to apply k-means clustering, utilizing a custom weighted distance function to group anime based on similarity and allowing for prioritization of anime properties based on weights.
- Designed the recommendation system to be adaptable and scalable, capable of expanding with input from k-means clustering and feature tensors. This approach allows for future handling of various content types.
- Created a simple and easy to use UI for users to browse anime and view recommendations using React, TailwindCSS and DaisyUI.
- Utilized React Router to create static and dynamic routes, displayed as individual pages.
- Deployed a live version of the website using Firebase Hosting.
- Utilized GitHub Actions to create a CI pipeline to lint and prettier format files.
- Developed a script to initialize data files and supplement missing data through the JikanAPI.
- Created user friendly and comprehensive documentation for the project using Docusaurus and hosted it via GitHub Pages.