The implementation of the paper:
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
Arxiv: https://arxiv.org/abs/2004.01646
Author: Bo Peng (peng.707@buckeyemail.osu.edu)
Feel free to send me an email if you have any questions.
- python 3.7.3
- PyTorch (version: 1.4.0)
- numpy (version: 1.16.2)
- scipy (version: 1.2.1)
- sklearn (version: 0.20.3)
Please download the data from the url in the paper and refer to the scripts/preprocessing_TaFeng.py script for the data preprocessing. We also uploaded all the processed datasets in the processed_data.zip for the seek of reproducibility.
Please refer to the scripts/create_jobs_github.sh script for hyper parameter tuning. This script could generate different jobs for different hyper parameter configurations. After generating jobs, you could run multiple jobs parallely using perl scripts (e.g. Drone.pl).
Please refer to the following example on how to train and evaluate the model (you are strongly recommended to run the program on a machine with GPU):
python main.py --dataset=TaFeng --decay=0.6 --l2=1e-2 --dim=32 --numIter=150 --model=SNBR --isTrain=1 --k=0 --testOrder=1 --isPreTrain=0 --batchSize=100 --mode='time_split'
For the baseline methods implemented by us (e.g., Dream and FPMC), you could reproduce the results by simply specifying the name of the model in main.py, and use the hyper parameters presented in the paper. For Sets2Set, we used the source code provided by the authors, you could find the source code here.