Ordnmf
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Graph2vec | 791 | 2 years ago | 1 | gpl-3.0 | Python | |||||
| A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017). | ||||||||||
| Daisyrec | 496 | a year ago | 16 | August 14, 2022 | mit | Python | ||||
| This is the repository of our article published in RecSys 2020 "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" and of several follow-up studies. | ||||||||||
| Gemsec | 234 | 2 years ago | gpl-3.0 | Python | ||||||
| The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019). | ||||||||||
| Recommender_system_with_python | 124 | a year ago | Jupyter Notebook | |||||||
| recommender system tutorial with Python | ||||||||||
| Probabilistic Matrix Factorization | 92 | 5 years ago | 3 | apache-2.0 | Python | |||||
| Python Implementation of Probabilistic Matrix Factorization(PMF) Algorithm for building a recommendation system using MovieLens ml-100k | GroupLens dataset | ||||||||||
| Retailbox | 34 | a year ago | mit | Python | ||||||
| 🛍️RetailBox - eCommerce Recommender System using Machine Learning | ||||||||||
| Boostedfactorization | 31 | 2 years ago | gpl-3.0 | Python | ||||||
| An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019). | ||||||||||
| Pmf Automl | 27 | 5 years ago | 1 | bsd-3-clause | Python | |||||
| Data and code related to the paper "Probabilistic matrix factorization for automated machine learning", NIPS, 2018. | ||||||||||
| Ionmf | 24 | 5 years ago | 9 | October 08, 2015 | 3 | other | Python | |||
| Integrative orthogonal non-negative matrix factorization | ||||||||||
| Apne | 22 | 6 years ago | mit | C | ||||||
| Code of the paper "Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective" | ||||||||||
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