LATTICE

PyTorch implementation for ACM Multimedia 2021 paper: Mining Latent Structures for Multimedia Recommendation

Dependencies

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

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.

Usage

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

Citation

If you want to use our codes in your research, please cite:

@inproceedings{LATTICE21,
  title     = {Mining Latent Structures for Multimedia Recommendation},
  author    = {Zhang, Jinghao and 
               Zhu, Yanqiao and 
               Liu, Qiang and
               Wu, Shu and 
               Wang, Shuhui and 
               Wang, Liang},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  pages     = {3872–3880},
  year      = {2021}
}

Acknowledgement

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