SMORE: Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation

Introduction

This is the Pytorch implementation for our WSDM 2025 paper:

[WSDM 2025] Rongqing Kenneth Ong, Andy W. H Khong (2025). Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation

Enviroment Requirement

• Python 3.7
• Pytorch 1.13

Dataset

Download from Google Drive: Baby/Sports/Clothing
The data comprises text and image features extracted from Sentence-Transformers and CNN.

How to run

1. Place the downloaded data (e.g. baby) into the data directory.
2. Enter the src folder and execute the following command:
   python main.py -m SMORE -d baby

Other parameters can be set either through the command line or by using the configuration files located in configs/model/SMORE.yaml and configs/dataset/*.yaml.

Performance Comparison

Best hyperparameters for reproducibility

We present the optimal hyperparameters for SMORE to replicate the results shown in Table 2 of our paper:

Datasets	n_ui_layers	n_layers	image_knn_k	text_knn_k	cl_loss	reg_weight	dropout_rate
Baby	4	1	40	15	0.01	1e-04	0.1
Sports	3	1	10	10	0.03	1e-04	0
Clothing	3	1	40	10	0.01	1e-05	0

Citation

If you find SMORE useful in your research, please consider citing our paper.

@article{ong2024spectrum,
  title={Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation},
  author={Ong, Rongqing Kenneth and Khong, Andy WH},
  journal={arXiv preprint arXiv:2412.14978},
  year={2024}
}

This code is made available solely for academic research purposes.

Acknowledgement

The structure of this code is inspired by the MMRec framework. We acknowledge and appreciate their valuable contributions.