"EliMRec: Eliminating Single-modal Bias in Multimedia Recommendation"
Xiaohao Liu, Zhulin Tao, Jiahong Shao, Lifang Yang, Xianglin Huang, ACMMM, June 2022
We explored single-modal bias by revealing the inner working of multi-modal fusion and achieved a generic framework EliMRec so as to eliminate single-modal bias in multimedia recommendation.
The code has been tested running under Python 3.6.5. The required packages are as follows:
- torch==1.7.0
- numpy==1.16.1
- torch_geometric==1.6.1
We provide three processed datasets: Kwai, Tiktok, and Movielnes.
- You can find the full version of recommendation datasets via Kwai, Tiktok, and Movielens. Since the copyright of datasets, we cannot release them directly.
The hyper-parameters used to train the models are set as default in the conf/EliMRec.properties. Feel free to change them if needed.
python3 main.py --recommender="EliMRec" --data.input.dataset=tiktok
@inproceedings{EliMRec,
author = {Liu, Xiaohao and
Tao, Zhulin and
Shao, Jiahong and
Yang, Lifang and
Huang, Xianglin},
title = {EliMRec: Eliminating Single-modal Bias in Multimedia Recommendation},
year = {2022},
publisher = {Association for Computing Machinery},
doi = {10.1145/3503161.3548404},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
series = {MM '22}
}