PyTorch implementation for "Mining Latent Structures with Contrastive Modality Fusion for Multimedia Recommendation"

Related tags

Deep Learning MICRO
Overview

MIRCO

PyTorch implementation for paper: Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation

Dependencies

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

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:

@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}
}

Acknowledgement

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

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Comments
  • I'm struggling with the model reproduction.

    I'm struggling with the model reproduction.

    Hi.

    Fist of all, thank you for you guys opened this repository. I learned alot with the paper and this code.

    image

    Before I try to injection my idea in this code, I wanted to reproduce the orignal model performance as you mentioned in the paper. But I've been being struggling with it. So, If you don't mind plz give me some advise about reproducing the model.

    Here is what my experiment setting is.

    • Using the preprocessed data shared on your google drive.

    • Dataset sports

    • n_users 35598

    • n_items 18357

    • n_interactions 268244

    • lr 1e-5
    • embed_size 64
    • layers 2
    • lambda_coeff=0.9
    • cf_model 'lightgcn'
    • mess_dropout=[0.1, 0.1]
    • regs=[1e-4, 1e-4, 1e-2]
    • sparse=1
    • loss_ratio=0.03
    • norm_type='sym'

    This is my best score i got image

    Best regards. Hoyoon.

    opened by hybyun0121 1
Owner
Big Data and Multi-modal Computing Group, CRIPAC
Big Data and Multi-modal Computing Group, Center for Research on Intelligent Perception and Computing
Big Data and Multi-modal Computing Group, CRIPAC
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