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https://github.com/AmazingDD/daisyRec
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)
https://github.com/AmazingDD/daisyRec
afm biasmf cdae collaborative-filtering deepfm ease factorization-machines item2vec k-nearest-neighbors matrix-factorization neumf neural-collaborative-filtering nfm pytorch recommender-system slim svdpp vae
Last synced: 3 months ago
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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)
- Host: GitHub
- URL: https://github.com/AmazingDD/daisyRec
- Owner: AmazingDD
- License: mit
- Created: 2019-12-02T04:58:34.000Z (almost 5 years ago)
- Default Branch: dev
- Last Pushed: 2023-03-28T23:36:26.000Z (over 1 year ago)
- Last Synced: 2024-07-24T04:07:56.620Z (4 months ago)
- Topics: afm, biasmf, cdae, collaborative-filtering, deepfm, ease, factorization-machines, item2vec, k-nearest-neighbors, matrix-factorization, neumf, neural-collaborative-filtering, nfm, pytorch, recommender-system, slim, svdpp, vae
- Language: Python
- Homepage:
- Size: 12.8 MB
- Stars: 546
- Watchers: 13
- Forks: 86
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - AmazingDD/daisyRec
README
[](https://github.com/AmazingDD/daisyRec)
[](https://arxiv.org/abs/2206.10848)
### Overview
daisyRec is a Python toolkit developed for benchmarking top-N recommendation task. The name DAISY stands for multi-**D**imension f**A**ir compar**I**son for recommender **SY**stem.
The figure below shows the overall framework of DaisyRec-v2.0.
This repository is used for publishing. If you are interested in details of our _experiments ranking results_, try to reach this [**repo file**](https://github.com/recsys-benchmark/DaisyRec-v2.0/blob/dev/appendix.pdf).
We really appreciate these repositories to help us improve the code efficiency:
- [RecSys2019_DeepLearning_Evaluation](https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation)
- [RecBole](https://github.com/RUCAIBox/RecBole)
### 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 into `daisyRec/`.
- The GUI Command Generator for `test.py` and `tune.py`, which can assist you to quikly write arguments and run the fair comparison experiments, is now available [**here**](https://daisyrec.netlify.app/).
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](https://daisyrec.readthedocs.io/en/latest/), which provides detailed explanations for all arguments.
### Implemented Algorithms
Models in daisyRec only take triples __ 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](https://grouplens.org/datasets/movielens/100k/) / [1M](https://grouplens.org/datasets/movielens/1m/) / [10M](https://grouplens.org/datasets/movielens/10m/) / [20M](https://grouplens.org/datasets/movielens/20m/)
- [Netflix Prize Data](https://archive.org/download/nf_prize_dataset.tar)
- [Last.fm](https://grouplens.org/datasets/hetrec-2011/)
- [Book Crossing](https://grouplens.org/datasets/book-crossing/)
- [Epinions](http://www.cse.msu.edu/~tangjili/trust.html)
- [CiteULike](https://github.com/js05212/citeulike-a)
- [Amazon-Book/Electronic/Clothing/Music ](http://jmcauley.ucsd.edu/data/amazon/links.html)(ratings only)
- [Yelp Challenge](https://kaggle.com/yelp-dataset/yelp-dataset)
### 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}
}
```