content-based-filtering 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
Content-based movie recommender: a training pipeline (TF-IDF, SVD, top-K cosine neighbours) and a Django app with search, autocomplete and a JSON API. Small models, about 1 ms lookups, ships with a demo model.
Transforming skincare recommendations: our hybrid system combines KNN, CNN, and EfficientNet B0 for personalized advice. Published in IEEE, with 80% validation accuracy and 87.10% training accuracy.
Privacy-first personalized recommendations for Jellyfin - local watch history analysis with zero tracking
A repository for a machine learning project about developing a hybrid movie recommender system.
Objective of the project is to build a hybrid-filtering personalized news articles recommendation system which can suggest articles from popular news service providers based on reading history of twitter users who share similar interests (Collaborative filtering) and content similarity of the article and user’s tweets (Content-based filtering).
Generate AI-powered movie recommendations, discover insightful profile statistics, pick movies from your watchlist, and see your film compatibility with friends
Movie recommendation system with Python. Implements content-based filtering (TF-IDF + cosine similarity), collaborative filtering with matrix factorization (TruncatedSVD), and a hybrid approach. Evaluates with Precision@K, Recall@K, and NDCG. Includes rating distribution plots, top movies, and sample recommendations.
MelodyMind offers personalized music recommendations, from a real-time Last.fm API-powered app to an advanced hybrid system combining content-based and collaborative filtering with LightFM.
A react native(UI), FastAPI (Server) and MySQL(DB) non-fungible token market place with a machine learning content-based filtering recommendation engine.
Hybrid book recommendation system fusing a scikit-learn k-nearest-neighbors collaborative filtering model with a TF-IDF cosine-similarity content model, weighted-average combined and trained on the 1.1 million rating Book-Crossing dataset, served through a FastAPI backend with TTL caching, a decoupled React frontend, and Docker Compose deployment.
🎬 Create an ML-powered movie recommendation system that uses content-based filtering to deliver 30 clever, funny, and tailored film picks based on your favorite movie. Harness Python, pandas, and scikit-learn—with a sprinkle of humor—to spark epic movie nights and uncover hidden cinematic treasures you’ll adore.
yogsgehlot/Chatbot-Song-Recommender-System
Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities
Code repo of solution of 11th place in Recsys Challenge 2022
Collaborative Filtering NN and CNN based recommender implemented with MXNet
Proyek akhir recommendation system untuk membangun model machine learning yang dapat memberi top-N anime rekomendasi
Hybrid Movie Recommendation System built with Python, Pandas, NumPy and Machine Learning techniques.
Posts/Feeds recommendation engine based on content based and collaborative filtering methods
A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise
DS307.N11 - Hệ Khuyến Nghị