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

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

Tool for simplifying to perform experiments with collaborative filtering models

Product Recommendation Platform Using Machine Learning and Uncertainty Modeling: Content-Based Filtering, Collavorative Filtering, Fuzzy logic, Sentiment analysis, Models probablistic, Association rules.

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

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

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.

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

泰迪在线实习备份

RecAnthology is a hybrid recommendation system for books, movies, and TV shows that combines content-based modeling and collaborative filtering with adaptive weighting, cold-start handling, and offline evaluation metrics to deliver measurable, personalized recommendations.

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

Noice-inspired hybrid audio recommendation system using public catalog-style metadata and synthetic listening events.

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

3 of the simplest recomendation system. Colaborative filter, content filter and hybrid filters

Bitcoin Core + StatsD integration Google Colab