Kelbrum

Kelbrum is an anime recommendation system designed to suggest anime titles similar to those chosen by users. It uses K-means++ clustering with a custom distance function, which is a combination of the Manhattan and Dice distance. The custom distance function assigns weighted values to each property of an anime such as its type, genres, score to compute the distance between two separate anime.

The frontend of the project was built using Vite with React, TanStack Router, TailwindCSS and DaisyUI.

The backend of this project was built utilizing Tensorflow.js in combination with external libraries such as ml-kmeans, ml-distance, and simple-statistics. Additionally, to perform TF-IDF analysis on anime synopses, natural was used alongside remove-stopwords, word-list, and lemmatizer.

Upon combining these two parts, the project comes together in the form that is Kelbrum.

The contents of this repository are licensed under the terms and conditions of the MIT license.

MIT © 2024-present Visakan Kirubakaran.