⭕️ Building Recommendation Engines
-
Updated
May 1, 2023 - Jupyter Notebook
⭕️ Building Recommendation Engines
store my personal project
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
Implicit Event Based Recommendation Engine for Ecommerce
Hybrid recommendation system using LightFM library and different loss functions on retail data.
Learn Data Science with Python
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.
WordPress Posts Recommend System based on Collaborative Filtering.
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.
Sistema de Recomendacion de la plataforma Steam desarrollado
hybrid recommender system using lightfm
Challenge recomendador - Campus Party Argentina 2021
Movie recommendation system
Common Machine Learning Examples 💻
Add a description, image, and links to the lightfm topic page so that developers can more easily learn about it.
To associate your repository with the lightfm topic, visit your repo's landing page and select "manage topics."