Anime Recommender adaptation of the BERTRec project with custom anime ratings dataset consisted of 54M ratings and 560000 users.

Advanced anime recommendation

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.

Web demo code of AnimeRecBert repo

Find the perfect anime to watch with friends!

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.

Python协同过滤算法在线动漫推荐系统，推荐动漫：用户没有登录，采用基于流行度的热点推荐，推荐点击量较多的动漫；用户已经登录，采用基于用户与基于物品的协同过滤推荐算法，如果基于用户与基于物品的协同过滤推荐算法均没有推荐结果，采用兴趣标签推荐，随机查询当前登录用户的兴趣标签中的动漫，同时过滤当前登录用户已经评分、收藏、点赞的动漫。

One-Stop Recommendation System

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.