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
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。
Web demo code of AnimeRecBert repo
Find the perfect anime to watch with friends!
One-Stop Recommendation System
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.
An exquisite AI-powered Anime Recommender and Personal Tracking Library. Built with React and Node.js, leveraging Google Gemini Pro & Flash for intelligent recommendations, dynamic metadata extraction, and seamless MyAnimeList / AniList library synchronization.
A hybrid Anime Recommendation System that combines Content-Based Filtering and Collaborative Filtering (SVD) to deliver accurate, diverse, and personalized anime suggestions.
MY Anime 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.
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