There are 10 repositories under movielens-dataset topic.
Factorization Machine models in PyTorch
An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models
Data cleaning, pre-processing, and Analytics on a million movies using Spark and Scala.
A ready-to-use framework of the state-of-the-art models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, anomaly detection, and etc.
Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques
This is a python project where using Pandas library we will find correlation and give the best recommendation for movies.
Movie Recommendation System: Project using R and Machine learning
Using Hybrid Fuzzy logic and Genetic Algorithms to build a faster and accurate recommender system.
It is a movie recommender web application which is developed using the Python.
Built a Movie Recommendation System using AutoEncoders.It was built using MovieLens Dataset
Training Deep AutoEncoders for Collaborative Filtering
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
Movie Recommendation System using the MovieLens dataset
Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
A Content Based And A Hybrid Recommender System using content based filtering and Collaborative filtering
A recommendation algorithm implemented with Biased Matrix Factorization method using tensorflow and tested over 1 million Movielens dataset with state-of-the-art validation RMSE around ~ 0.83
Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities
Basic recommendation system for Movilens dataset using Keras
Neural Collaborative Filtering with MovieLens in pytorch
Recommender System (Java, Apache Spark)
deep learning project
This is a web application for movie recommendation based on Flask, HTML and Python
An on-line movie recommender using Spark, Clojure Luminus, and the MovieLens dataset
Comparison of performance evaluation of the baseline and hybrid recommendation systems using various metrics, to prove that hybrid systems perform better
Exploratory data analysis of movielesns-1m dataset
A simple ML movie recommender engine
Implementing Model-Based CF using SVD & Memory-Based CF by computing cosine similarity on MovieLens dataset
A recommendation algorithm using the MovieLens dataset.