⭕️ Building Recommendation Engines
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Updated
May 1, 2023 - Jupyter Notebook
⭕️ Building Recommendation Engines
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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.
An realtime recommendation system supporting online updates
Pre-train Embedding in LightFM Recommender System Framework
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
A hybrid recommender system for suggesting CDN (content delivery network) providers to various websites
Built a hybrid recommendation system with LightFM library and customised loss functions to optimize performance on retail data."
Implicit Event Based Recommendation Engine for Ecommerce
A recommendation system that recommends artists to users.
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.
Learn Data Science with Python
✨ Recommendation Systems Using Diverse Techniques ✨
✨ Recommendation Systems Using Diverse Techniques ✨
WordPress Posts Recommend System based on Collaborative Filtering.
Common Machine Learning Examples 💻
Movie recommendation system
Challenge recomendador - Campus Party Argentina 2021
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