Awesome

MIRCO

This is the code for the Paper: Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation.

Usage

Dataset Preparation

• Download 5-core reviews data, meta data, and image features from Amazon product dataset. Put data into the directory data/meta-data/.

• Install sentence-transformers and download pretrained models to extract textual features. Unzip pretrained model into the directory sentence-transformers/:

  ├─ data/: 
      ├── sports/
      	├── meta-data/
      		├── image_features_Sports_and_Outdoors.b
      		├── meta-Sports_and_Outdoors.json.gz
      		├── reviews_Sports_and_Outdoors_5.json.gz
      ├── sentence-transformers/
          	├── stsb-roberta-large
  

• Run python build_data.py to preprocess data.

• Run python cold_start.py to build cold-start data.

• We provide processed data Baidu Yun (access code: m37q), Google Drive.

Quick start

Start training and inference as:

cd codes
python main.py --dataset {DATASET}

For cold-start settings:

python main.py --dataset {DATASET} --core 0 --verbose 1 --lr 1e-5

Requirements

• Python 3.6
• torch==1.5.0
• scikit-learn==0.24.2
• torch-scatter==2.0.8

Citation

Please cite our paper if you use the code:

@article{zhang2022latent,
  title={Latent structure mining with contrastive modality fusion for multimedia recommendation},
  author={Zhang, Jinghao and Zhu, Yanqiao and Liu, Qiang and Zhang, Mengqi and Wu, Shu and Wang, Liang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2022},
  publisher={IEEE}
}

Acknowledge

The structure of this code is largely based on LightGCN. Thank for their work.