Overview
daisyRec is a Python toolkit developed for benchmarking top-N recommendation task. The name DAISY stands for multi-Dimension fAir comparIson for recommender SYstem. (Please note that DaisyRec is still under testing. If there is any issue, please feel free to let us know)
The figure below shows the overall framework of DaisyRec.
This repository is used for publishing. If you are interested in details of our experiments ranking results, try to reach this repo file.
We really appreciate these repositories to help us improve the code efficiency:
How to Run
Make sure you have a CUDA enviroment to accelarate since the deep-learning models could be based on it.
1. Install from pip
pip install daisyRec
2. Clone from github
git clone https://github.com/AmazingDD/daisyRec.git && cd daisyRec
-
Example 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 available here.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!
Documentation
The documentation of DaisyRec is available here, which provides detailed explanations for all arguments.
Implemented Algorithms
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 |
Datasets
You can download experiment data, and put them into the data 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
Cite
Please cite both of the following papers if you use DaisyRec 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}
}
