Crab |
Python |
RS |
BSD |
|
CARSKit |
Java |
CARS |
GPL |
Yong Zheng, Bamshad Mobasher, Robin Burke. "CARSKit: A Java-Based Context-aware Recommendation Engine", Proceedings of the 15th IEEE International Conference on Data Mining (ICDM) Workshops, pp. 1668-1671, Atlantic City, NJ, USA, Nov 2015 |
DeepCTR |
Python |
ML |
Apache 2.0 |
Weichen Shen, DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models, 2017 |
EasyRec |
Python |
RS |
Apache-2.0 |
EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems, AAAI'23 |
ELLIOT |
Tensorflow |
RS |
Apache 2.0 |
Anelli, Vito Walter and Bellogin, Alejandro and Ferrara, Antonio and Malitesta, Daniele and Merra, Felice Antonio and Pomo, Claudio and Donini, Francesco Maria and Di Noia, Tommaso, Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation, SIGIR'21 |
fastFM |
Python |
ML |
|
Bayer, Immanuel. "fastFM: A library for factorization machines." Journal of Machine Learning Research 17.184 (2016): 1-5. |
LightFM |
Python |
RS |
Apache 2.0 |
Maciej Kula, Metadata Embeddings for User and Item Cold-start Recommendations. Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with 9th {ACM} Conference on Recommender Systems (RecSys 2015), Vienna, Austria, September 16-20, 2015. |
LibRec |
Java |
RS |
GPL |
Guo, Guibing, et al. "LibRec: A Java Library for Recommender Systems." UMAP Workshops. 2015. |
LensKit |
Java |
RS |
LGPL |
Ekstrand, Michael D., et al. "LensKit: a modular recommender framework." Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011. |
LODRecLib |
Java |
LODRS |
MIT |
Noia, Tommaso Di, et al. "Sprank: Semantic path-based ranking for top-n recommendations using linked open data." ACM Transactions on Intelligent Systems and Technology (TIST) 8.1 (2016): 9. |
libFM |
C++ |
ML |
GPL |
Steffen Rendle (2012): Factorization Machines with libFM, in ACM Trans. Intell. Syst. Technol., 3(3), May |
MTRecLib |
PyTorch |
MTRS |
|
"Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising" KDD'21 |
MyMedialite |
C# |
RS |
GPL |
Gantner, Zeno, et al. "MyMediaLite: A free recommender system library." Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011. |
Mahout |
Java |
ML |
Apache 2.0 |
|
MMRec |
Python |
MMRS |
GPL 3.0 |
Hongyu Zhou, et al. "A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions". arXiv:2302.04473 |
NeuRec |
Python, Tensorflow |
RS |
MIT |
|
OpenLearning4DeepRecSys |
Tensorflow |
RS |
|
|
python-recsys |
Python |
RS |
|
|
QRec |
Tensorflow |
RS |
|
|
recmetrics |
Python |
Metrics |
MIT |
|
Recommender-System |
Tensorflow |
RS |
MIT |
|
RankSys |
Java |
RS |
MPL |
https://github.com/RankSys/RankSys/wiki/References |
RankLib |
Java |
RS |
BSD |
|
RecBole |
PyTorch |
RS |
MIT |
Wayne Xin Zhao et. al, RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms, CIKM'21 |
recommenderlab |
R |
RS |
|
Michael Hahsler (2022) recommenderlab: An R framework for developing and testing recommendation algorithms. arXiv:2205.12371 |
SELFRec |
PyTorch, Tensorflow |
SSL |
|
Yu, Junliang et. al, Self-Supervised Learning for Recommender Systems: A Survey |
Tensorflow Recommenders |
Tensorflow |
RS |
Apache 2.0 |
|
TensorRec |
Tensorflow |
RS |
Apache 2.0 |
|
tffm |
Tensorflow |
RS |
MIT |
|