Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Recommenders | 17,972 | 2 | 4 days 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) |