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[ICASSP'2025] "M³Rec: Selective State Space Models with Mixture-of-Modality Experts for Multi-Modal Sequential Recommendation"

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# M<sup>3</sup>Rec: Selective State Space Models with Mixture-of-Modality Experts for Multi-Modal Sequential Recommendation

This is our Pytorch implementation for ICASSP 2025 paper [M<sup>3</sup>Rec: Selective State Space Models with Mixture-of-Modality Experts for Multi-Modal Sequential Recommendation](https://ieeexplore.ieee.org/abstract/document/10887582).
<p align="center">
<img src="./img/Framework.png" alt="M3Rec" />
</p>

> 🚀🚀M<sup>3</sup>Rec is a new multimodal sequential recommendation framework that integrates a <u>M</u>amba-based selective state space model with <u>M</u>ixture-of-<u>M</u>odality Experts.M<sup>3</sup>Rec strengthens the modeling of user action sequence dependencies through shared Mamba blocks across modalities and employs modality experts to extract modality-specific user preferences.

<h2>Dependencies </h2>

All experiments are conducted on an NVIDIA 24GB 3090 GPU. The required packages are as follows:
* 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`

<h2>Datasets</h2>

> 📢📢We provide public three pre-processed datasets requiring no additional processing on [Google Drive](https://drive.google.com/drive/folders/1OzJ_5zLaP-5feF6otRDlDsMLWxci3syL?usp=drive_link). You can download and place them in the "./dataset".

>✨✨To facilitate research in multimodal sequential recommendation systems, we provide code for preprocessing the [original Amazon datasets](http://jmcauley.ucsd.edu/data/amazon/links.html) in "preprocess/data_preprocess.ipynb". If you find this resource useful for your work, please kindly cite our work.

```python run_M3Rec.py```

> 🔥🔥In our experiments, we observed significant performance differences across different python environments. Through detailed analysis, we analyze these discrepancies from version differences between the Mamba's required dependency "causal-conv1d" and "mamba-ssm". To ensure transparency and reproducibility, we have released the training logs under the "./best_log" and corresponding model weights in [Google Drive](https://drive.google.com/drive/folders/1YHZKPOb10yw2zHVPkSFCQcSUQseIngF6?usp=drive_link).

## Citation

If you find this work helpful to your research, please kindly consider citing our paper.

```
@inproceedings{guo2025m,
  title={M 3 Rec: Selective State Space Models with Mixture-of-Modality Experts for Multi-Modal Sequential Recommendation},
  author={Guo, Xu and Zhang, Tong and Xue, Yufei and Wang, Chenxu and Wang, Fuyun and Cui, Zhen},
  booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2025},
  organization={IEEE}
}
```

## Acknowledgment

This project is based on [Mamba4Rec](https://github.com/chengkai-liu/Mamba4Rec), [Mamba](https://github.com/state-spaces/mamba), [Causal-Conv1d](https://github.com/Dao-AILab/causal-conv1d), and [RecBole](https://github.com/RUCAIBox/RecBole). Thanks for their excellent works.

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