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Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)
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AmazingDD/daisyRec
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daisyRec is a Python toolkit developed for benchmarking top-N recommendation task. The name DAISY stands for multi-Dimension fAir comparIson for recommenderSYstem.
The figure below shows the overall framework of DaisyRec-v2.0.
This repository is used for publishing. If you are interested in details of ourexperiments ranking results, try to reach thisrepo file.
We really appreciate these repositories to help us improve the code efficiency:
Make sure you have aCUDA enviroment to accelarate since the deep-learning models could be based on it.
pip install daisyRecgit clone https://github.com/AmazingDD/daisyRec.git && cd daisyRecExample codes are listed in
run_examples, try to refer them and find out how to use daisy; you can also implement these codes by moving them intodaisyRec/.The GUI Command Generator for
test.pyandtune.py, which can assist you to quikly write arguments and run the fair comparison experiments, is now availablehere.The generated command will be like this:
python tune.py --param1=20 --param2=30 ....python test.py --param1=20 --param2=30 ....We highly recommend you to implement the code with our GUI firstly!
The documentation of DaisyRec is availablehere, which provides detailed explanations for all arguments.
Models in daisyRec only take triples<user, item, rating> into account, so FM-related models will be specialized accrodingly.Below are the algorithms implemented in daisyRec. More baselines will be added later.
| Model | Publication |
|---|---|
| MostPop | A re-visit of the popularity baseline in recommender systems |
| ItemKNN | Item-based top-N recommendation algorithms |
| EASE | Embarrassingly Shallow Autoencoders for Sparse Data |
| PureSVD | Top-n recommender system via matrix completion |
| SLIM | SLIM: Sparse Linear Methods for Top-N Recommender Systems |
| MF | Matrix factorization techniques for recommender systems |
| FM | Factorization Machines |
| NeuMF | Neural Collaborative Filtering |
| NFM | Neural Factorization Machines for Sparse Predictive Analytics |
| NGCF | Neural Graph Collaborative Filtering |
| Multi-VAE | Variational Autoencoders for Collaborative Filtering |
| Item2Vec | Item2vec: neural item embedding for collaborative filtering |
| LightGCN | LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation |
You can download experiment data, and put them into thedata folder.All data are available in links below:
- MovieLens-100K /1M /10M /20M
- Netflix Prize Data
- Last.fm
- Book Crossing
- Epinions
- CiteULike
- Amazon-Book/Electronic/Clothing/Music(ratings only)
- Yelp Challenge
Please cite both of the following papers if you useDaisyRec in a research paper in any way (e.g., code and ranking results):
@inproceedings{sun2020are, title={Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison}, author={Sun, Zhu and Yu, Di and Fang, Hui and Yang, Jie and Qu, Xinghua and Zhang, Jie and Geng, Cong}, booktitle={Proceedings of the 14th ACM Conference on Recommender Systems}, year={2020}}@article{sun2022daisyrec, title={DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation}, author={Sun, Zhu and Fang, Hui and Yang, Jie and Qu, Xinghua and Liu, Hongyang and Yu, Di and Ong, Yew-Soon and Zhang, Jie}, journal={arXiv preprint arXiv:2206.10848}, year={2022}}About
Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)