@article{DBLP:journals/kbs/XuZPWYDZW23,
author = {Li Xu and
Jun Zeng and
Weile Peng and
Hao Wu and
Kun Yue and
Haiyan Ding and
Lei Zhang and
Xin Wang},
title = {Modeling and predicting user preferences with multiple item attributes
for sequential recommendations},
journal = {Knowl. Based Syst.},
volume = {260},
pages = {110174},
year = {2023},
url = {https://doi.org/10.1016/j.knosys.2022.110174},
doi = {10.1016/j.knosys.2022.110174},
timestamp = {Wed, 28 Jun 2023 14:32:09 +0200},
biburl = {https://dblp.org/rec/journals/kbs/XuZPWYDZW23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
To use the code, enter the models directory and execute run_Model.py such as:
cd models/Caser
python run_Caser.py
SASRec: python main.py --dataset=ml-1m --train_dir=default --maxlen=200 --dropout_rate=0.2 --device=cuda
SSE-PT: python3 main.py --maxlen=200 --dropout_rate 0.2 --threshold_user 1 --threshold_item 1 --device=cuda
Note: Due to the different sample construction methods and experimental methods of different algorithms, we generate independent codes for each algorithm.
- Tensorflow 1.1+
- Python 3.6+,
- numpy
- pandas
- More models
- Code refactoring
- Support tf.data.datasets and tf.estimator