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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.
This example uses the lightfm recommender system library to train a hybrid content-based + collaborative algorithm that uses the WARP loss function on the movielens dataset
Recommendation engine with a .97 AUC achieved using clustering techniques to create user features. Data represents Olist marketplace transactions and was retrieved from kaggle.com.
This project is focused on building a movie recommendation system using the MovieLens dataset. The system leverages several machine learning techniques to provide personalized movie recommendations based on user preferences and past behaviors.