This website applies a recommendation system and continuous learning.

Introducción al Aprendizaje No Supervisado en Español

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

Research about Massive Data Processing Techniques in Data Science

Tool for simplifying to perform experiments with collaborative filtering models

A hybrid movie recommendation system developed with data from The Movie Database (TMDb)

This Repo contains The Implementation of Content-Based Recommended System and Collaborative Filtering Recommended System for movies dataset in python.

Sistema de Recomendacion de la plataforma Steam desarrollado

泰迪在线实习备份

The is a course project, a within-class kaggle competition targeting to recommend the courses to the potential buyer.

✨🎬 Explore seu Próximo Filme Favorito! Um sistema de recomendação colaborativa que personaliza sua experiência de entretenimento. 🍿✨

Recommender System Project This repository contains the implementation of various recommender system algorithms, including KNN, SVM, Decision Tree, and Matrix Factorization. The primary focus is on Matrix Factorization to provide personalized movie recommendations using the MovieLens dataset.

Recommendation Systems project from ML and BD Master Degree from UPM. Collaborative and content-based filtering competitions.

Backbones for Ranking and RecSys tasks

Collaborative Filtering and Association Rule Mining App

Popularity based, Content based recommender & Colaborative Filtering systems

Recommendation system using collaborative filtering applied on e-commerce websites

This project builds a scalable recommendation system inspired by Netflix, leveraging collaborative filtering and deep learning techniques to predict user preferences. It focuses on large-scale data processing, model training, and performance evaluation for personalized content recommendations.

A new approach in designing and developing a collaborative-interactive movie recommender system based on user ratings