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.
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).
A react native(UI), FastAPI (Server) and MySQL(DB) non-fungible token market place with a machine learning content-based filtering recommendation engine.
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.
🎬 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.
Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities
Collaborative Filtering NN and CNN based recommender implemented with MXNet
Code repo of solution of 11th place in Recsys Challenge 2022
Proyek akhir recommendation system untuk membangun model machine learning yang dapat memberi top-N anime rekomendasi
Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows
A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise
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.
Recommending movies to user using various Colaborative Filtering and Content Based Filtering.
recommending recipes with content-based filtering approach
Posts/Feeds recommendation engine based on content based and collaborative filtering methods
Recommendation System for Amazon Alexa E-Commerce Application
DS307.N11 - Hệ Khuyến Nghị
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