An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
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Updated
Oct 6, 2021 - Jupyter Notebook
An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
Factorization Machine models in PyTorch
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.
Movie Recommendation System: Project using R and Machine learning
tf-recsys contains collaborative filtering (CF) model based on famous SVD and SVD++ algorithm. Both of them are implemented by tensorflow in order to utilize GPU acceleration.
It is a movie recommender web application which is developed using the Python.
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.
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
Using Hybrid Fuzzy logic and Genetic Algorithms to build a faster and accurate recommender system.
Movie Recommendation System using the MovieLens dataset
Personalized real-time movie recommendation system
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.
Built a Movie Recommendation System using AutoEncoders.It was built using MovieLens Dataset
Analysis of MovieLens Dataset in Python
Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
Basic recommendation system for Movilens dataset using Keras
Exploring the MovieLens Dataset with pySpark
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
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