Anime Recommender adaptation of the BERTRec project with custom anime ratings dataset consisted of 54M ratings and 560000 users.
This is a Content-Based Anime Recommendation System built with Python. It recommends anime based on genre similarity using TF-IDF vectorization and cosine similarity via the k-Nearest Neighbors algorithm.
Advanced anime recommendation
Web demo code of AnimeRecBert repo
This project is a web-based anime recommendation system that uses machine learning techniques to suggest anime titles based on user preferences such as genre, type, and rating. It employs content-based filtering using TF-IDF and cosine similarity to compute relevant matches from a dataset of anime.
One-Stop Recommendation System
Find the perfect anime to watch with friends!
It is a system that uses artificial intelligence to suggest movies and anime based on a user’s preferences. It analyzes factors such as genre, themes and ratings to generate personalized recommendations. By combining ML, NLP, and multiple algorithms, it helps users quickly discover content they are likely to enjoy.
MY Anime Recommendation System
A hybrid Anime Recommendation System that combines Content-Based Filtering and Collaborative Filtering (SVD) to deliver accurate, diverse, and personalized anime suggestions.
Add a description, image, and links to the anime-recommendation-system topic page so that developers can more easily learn about it.
To associate your repository with the anime-recommendation-system topic, visit your repo's landing page and select "manage topics."