⭕️ Building Recommendation Engines

store my personal project

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 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

python package to help preparing Dataframes (csv ... ) for LightFM module for easy Training

A hybrid recommender system for suggesting CDN (content delivery network) providers to various websites

Pre-train Embedding in LightFM Recommender System Framework

Learn Data Science with Python

WordPress Posts Recommend System based on Collaborative Filtering.

Common Machine Learning Examples 💻

recsys course notebooks

with Kaggle datasets

Implicit Event Based Recommendation Engine for Ecommerce

Sistema de Recomendacion de la plataforma Steam desarrollado

Challenge recomendador - Campus Party Argentina 2021

Comparison of two approaches for building a recommender system presented. The first one is a collaborative filtering. The second one is hybrid recommender system. This project is the second stage of a contest for an internship in VK.

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.

This repository contains code I wrote in the Business Intelligence course at Universidad Mayor. The folders tarea-1x contain a small data modelling and data visualization exercise leveraging Oracle Cloud databases, while tarea-2 contains a movie recommender system based around the Netflix Prize dataset.

Рекомендательные системы

Task: predict whether users will like a social network post? LightFM + CatBoost