MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation

This repository provides a reference implementation of MONET as described in the following paper:

MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
Yungi Kim, Taeri Kim, Won-Yong Shin and Sang-Wook Kim
17th ACM Int'l Conf. on Web Search and Data Mining (ACM WSDM 2024)

Overview of MONET

Authors

• Yungi Kim (gozj3319@hanyang.ac.kr)
• Taeri Kim (taerik@hanyang.ac.kr)
• Won-Yong Shin (wy.shin@yonsei.ac.kr)
• Sang-Wook Kim (wook@hanyang.ac.kr)

Requirements

The code has been tested running under Python 3.6.13. The required packages are as follows:

• gensim==3.8.3
• pytorch==1.10.2+cu113
• torch_geometric==2.0.3
• sentence_transformers==2.2.0
• pandas
• numpy
• tqdm
• torch-scatter
• torch-sparse
• torch-cluster
• torch-spline-conv
• torch-geometric

Dataset Preparation

Dataset Download

Men Clothing and Women Clothing: Download Amazon product dataset provided by MAML. Put data folder into the directory data/.

Beauty and Toys & Games: Download 5-core reviews data, meta data, and image features from Amazon product dataset. Put data into the directory data/{folder}/meta-data/.

Dataset Preprocessing

Run python build_data.py --name={Dataset}

Usage

For simplicity, we provide usage for the Women Clothing dataset.

---

• For MONET in RQ1,

python main.py --agg=concat --n_layers=2 --alpha=1.0 --beta=0.3 --dataset=WomenClothing --model_name=MONET_2_10_3

---

• For RQ2, refer the second cell in "Preliminaries.ipynb".

---

• For MONET_w/o_MeGCN and MONET_w/o_TA in RQ3,

python main.py --agg=concat --n_layers=0 --alpha=1.0 --beta=0.3 --dataset=WomenClothing --model_name=MONET_wo_MeGCN
python main.py --target_aware --agg=concat --n_layers=2 --alpha=1.0 --beta=0.3 --dataset=WomenClothing --model_name=MONET_wo_TA

---

• For RQ4 (hyperparameters $\alpha$, $\beta$ sensitivity),

python main.py --agg=concat --n_layers=2 --alpha={value} --beta=0.3 --dataset=WomenClothing --model_name=MONET_2_{alpha}_3
python main.py --agg=concat --n_layers=2 --alpha=1.0 --beta={value} --dataset=WomenClothing --model_name=MONET_2_10_{beta}

Cite

We encourage you to cite our paper if you have used the code in your work. You can use the following BibTex citation:

@inproceedings{kim24wsdm,
  author   = {Yungi Kim and Taeri Kim and Won{-}Yong Shin and Sang{-}Wook Kim},
  title     = {MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation},
  booktitle = {ACM International Conference on Web Search and Data Mining (ACM WSDM 2024)},      
  year      = {2024}
}

Acknowledgement

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