fsansan / daisyrec

A developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks

Overview

DaisyRec is a Python toolkit dealing with rating prediction and item ranking issue.

The name DAISY (roughly :) ) stands for multi-Dimension fAirly compArIson for recommender SYstem.

To get all dependencies, run:

pip install -r requirement.txt

Before running, you need first run:

python setup.py build_ext --inplace

to generate .so or .pyd file used for further import.

Make sure you have a CUDA enviroment to accelarate since these deep-learning models could be based on it.

DaisyRec handled ranking issue mainly and split recommendation problem into point-wise ones and pair-wise ones so that different loss function are constructed such as BPR, Top-1, Hinge and Cross Entropy. All algorithms already implemented are exhibited below:

use main.py to achieve KPI results calculated by certain algorithm above. For example, you can implement this program to implement BPR-MF:

python main.py --problem_type=pair --algo_name=mf --loss_type=BPR --num_ng=2

TODO list

• weight initialization interface
• user-level time-aware fold-out method
• user-level/item-level/user-item-level Ncore

Cite

Here is a Bibtex entry if you ever need to cite DaisyRec in a research paper (please keep us posted, we would love to know if Daisy was helpful to you)

@misc{yudi2020daisyrec,
  title={DaisyRec},
  author={Yu Di},
  year={2020},
  publisher={GitHub},
  howpublished={\url{https://github.com/AmazingDD/daisyRec}},
}