PyTorch implementation for paper: Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation
- Python 3.6
- torch==1.5.0
- scikit-learn==0.24.2
- torch-scatter==2.0.8
-
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.
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
If you want to use our codes in your research, please cite:
@article{MICRO21,
title = {Latent Structures 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 = {arXiv.org},
year={2021},
eprint={2111.00678},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
The structure of this code is largely based on LightGCN. Thank for their work.