Project Name	Stars	Repos Using This	Packages Using This	Most Recent Commit	Total Releases	Latest Release	Open Issues	License	Language
Recommenders	17,739		2	a month ago	11	April 01, 2022	169	mit	Python
Best Practices on Recommendation Systems
Deeprec	1,068			2 years ago			8	gpl-3.0	Python
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
Datasets For Recommender Systems	821			8 months ago			1		Jupyter Notebook
This is a repository of a topic-centric public data sources in high quality for Recommender Systems (RS)
Recommendationraccoon	690	26	4	4 years ago	19	March 06, 2017	19	mit	JavaScript
A collaborative filtering based recommendation engine and NPM module built on top of Node.js and Redis. The engine uses the Jaccard coefficient to determine the similarity between users and k-nearest-neighbors to create recommendations. This module is useful for anyone with a database of users, a database of products/movies/items and the desire to give their users the ability to like/dislike and receive recommendations.
Goodbooks 10k	576			6 years ago			1	other	Jupyter Notebook
Ten thousand books, six million ratings
Dsin	405			a year ago			5	apache-2.0	Python
Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"
Caserecommender	367			2 years ago	42	November 25, 2021	4	mit	Python
Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems
Ripplenet	347			4 years ago			9	mit	Python
A tensorflow implementation of RippleNet
Recdb Postgresql	262			4 years ago			10		C
RecDB is a recommendation engine built entirely inside PostgreSQL
Recommender	249			2 years ago			1	bsd-2-clause	C
A C library for product recommendations/suggestions using collaborative filtering (CF)

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Best Practices on Recommendation Systems

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A collaborative filtering based recommendation engine and NPM module built on top of Node.js and Redis. The engine uses the Jaccard coefficient to determine the similarity between users and k-nearest-neighbors to create recommendations. This module is useful for anyone with a database of users, a database of products/movies/items and the desire to give their users the ability to like/dislike and receive recommendations.

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