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 file for macOS or .pyd file for WindowsOS 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
- DeepFM algorithm module
- AFM algorithm module
- NGCF algorithm module
- weight initialization interface