Factorization Machine models in PyTorch
-
Updated
Apr 8, 2024 - Python
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
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.
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
Personalized real-time movie recommendation system
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
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.
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
Any data but iris 👁
Movie Recommendation System using the MovieLens dataset
Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
Add a description, image, and links to the movielens-dataset topic page so that developers can more easily learn about it.
To associate your repository with the movielens-dataset topic, visit your repo's landing page and select "manage topics."