Machine Learning homework project at EPFL
-
Updated
Dec 28, 2017 - TeX
Machine Learning homework project at EPFL
Recommender system that applies a user-to-user collaborative filtering algorithm on the MAL dataset to recommend anime for users.
Getting a better grasp of recommender systems
Repository to demonstrate how to use machine learning to generate recommendations
Recommend Airbnb Listings to the User based on reviews.
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
Projeto Final de Graduação - Engenharia de Computação UNICAMP 2019. Revisão de Ténicas em Sistemas de Recomendação.
Recommendation Systems tutorial
This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.
Implementation of the model iGSLR
Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering
Recommender system with Netflix database using matrix factorization
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.
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.
A basic movie recommendation system using collaborative filtering methods on MoiveLens dataset.
Exploring Recommender Systems using various Machine Learning Models like scikit-learn, Surprise, NLP and collaborative filtering using KNN and Tensorflow.
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
Add a description, image, and links to the surprise-python topic page so that developers can more easily learn about it.
To associate your repository with the surprise-python topic, visit your repo's landing page and select "manage topics."