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.

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

Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows

Recommendation System for Amazon Alexa E-Commerce Application

Recommending movies to user using various Colaborative Filtering and Content Based Filtering.

recommending recipes with content-based filtering approach

Comparison of performance evaluation of the baseline and hybrid recommendation systems using various metrics, to prove that hybrid systems perform better

Design and implementation from scratch of different models for a musical recommendation system

DS307.N11 - Hệ Khuyến Nghị

This project associated with my university for milestone project. A book recommender system using k-means clustering with content based approach from goodreads book dataset.

Reference repository for the O-Horizon Recommendation Engine featured in Neo4j Graphversation Episode 2

🎬 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.

🎬 It helps you discover films. Search for your favorite movies, get a "Surprise Me" pick, and explore trending movies—all while viewing live details like posters, trailers, ratings, and cast information.

Book recommendation system using model development with Content-Based Filtering and Collaborative Filtering techniques.