Best Practices on Recommendation Systems
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Updated
Jul 10, 2024 - Python
Best Practices on Recommendation Systems
A movie recommendation system made with Python and Flask
a personalized, offline, imaginary social media feed
This is a fork version of NVIDIA Merlin. NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.
Board game recommendation engine
A TensorFlow recommendation algorithm and framework in Python.
OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
Contextual Movie Recommender System built on mobile with the aim of showing interest based movies from the huge amount of data based on rating of user and critics which would be crawled from the specified website. Recommender System are primarily directed towards individuals who lack sufficient personal experience or competence to evaluate the po…
A book recommendations application that works on the Dash framework and implements content based filtering using TF-IDF and cosine similarity.
Recommendation System for question / requirement and recommended answer from knowledge base
⚡ A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize
A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.
Finding recommendations between them all. Work in progress.
Code for RecSys'19 paper: Leveraging Post-click Feedback for Content Recommendations
matrix-factorization is a light-weight program written in python language for performing basic operations for matrix factorization-based collaborative filtering. I have plans to create a python module from this repository in the future. If you want to contribute to this project, you are most welcome.
Part of a presentation I gave at a career day at a local junior high. This is an example of an algorithm that will recommend movies.
A repository to practice with recommendation engines.
A lightweight recommendation algorithm framework based on LycorisNet.
Recommends movies based on user input and a a pre-trained NMF-model with a browser-based user interface (Flask). Optionally outputs movie trailers for recommendations from YouTube.
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