Getting a better grasp of recommender systems

Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering

This repository covers a project of creating a recommendation system using collaborative filtering on the Grouplens movielens database. The surprise library is utilized to test out different models (KNN Basic, KNN Baseline, and SVD). SVD was found to be the most accurate and then was implemented into the system. The cold start problem was addressed by giving new users the opportunity to rate a random sample of 5 movies from movies that are among the most popular.

Recommender system that applies a user-to-user collaborative filtering algorithm on the MAL dataset to recommend anime for users.

Recommender system with Netflix database using matrix factorization

Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.

This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.

Suprise-Python Wrapper for Persa.jl

This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.

Código-fonte desenvolvido para implementação da parte prática referente dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Ciência da Computação.

Yelp Recommender System Project

Implementation of the model iGSLR

A basic movie recommendation system using collaborative filtering methods on MoiveLens dataset.

Machine Learning homework project at EPFL

MovieLens recommended system project

This is a recommendation system based on Singe value Decomposition method ( Collaborative filters )

Recommend Airbnb Listings to the User based on reviews.

Recommender System built using Python, Angular, Firebase & MySQL

The goal of this project was to build an explicit recommender system using collaborative filtering for restaurants in Charlotte using Yelp's Open Dataset. I wanted to explore the mechanics of recommendations systems, and explore a new library in Surprise.

Repository to demonstrate how to use machine learning to generate recommendations

Recommendation Systems tutorial

Projeto Final de Graduação - Engenharia de Computação UNICAMP 2019. Revisão de Ténicas em Sistemas de Recomendação.

Exploring Recommender Systems using various Machine Learning Models like scikit-learn, Surprise, NLP and collaborative filtering using KNN and Tensorflow.