Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models (RelKD@KDD 2024 Best Paper Award Award)
Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, James Caverlee
Paper: https://arxiv.org/abs/2403.03900
- Python 3.7+
- PyTorch 1.12+
- CUDA 11.6+
- Install RecBole:
pip install recbole
- Install causal Conv1d and the core Mamba package:
pip install causal-conv1d>=1.2.0
pip install mamba-ssm
You can also refer to the required environment specifications in environment.yaml
.
python run.py
Specifying the dataset in config.yaml
will trigger an automatic download. Please set an appropriate maximum sequence length in config.yaml
for each dataset before training.
@article{liu2024mamba4rec,
title={Mamba4rec: Towards efficient sequential recommendation with selective state space models},
author={Liu, Chengkai and Lin, Jianghao and Wang, Jianling and Liu, Hanzhou and Caverlee, James},
journal={arXiv preprint arXiv:2403.03900},
year={2024}
}
This project is based on Mamba, Causal-Conv1d, and RecBole. Thanks for their excellent works.