BM3 (WWW'23)

Pytorch implementation for "Bootstrap Latent Representations for Multi-modal Recommendation"-WWW'23 Official ACM

• Trained logs&models are stored at: https://github.com/enoche/BM3/tree/master/trained-models-logs
• :twisted_rightwards_arrows: This model is integrated into the MMRec framework.
• :point_right: Check the awesome multimodal recommendation resources.

Overview of BM3

<p> <img src="./images/bm3.png" width="800"> </p>

Data

Download from Google Drive: Baby/Sports/Elec
The data already contains text and image features extracted from Sentence-Transformers and CNN.

How to run

1. Put your downloaded data (e.g. baby) under data dir.
2. Enter src folder and run with
   python main.py -m BM3 -d baby
   You may specify other parameters in CMD or config with configs/model/*.yaml and configs/dataset/*.yaml.

Best hyper-parameters for reproducibility

We report the best hyper-parameters of BM3 to reproduce the results in Table III of our paper as:

| Datasets | layers | dropout | reg_weight | |----------|--------|---------|------------| | Baby | 1 | 0.5 | 0.1 | | Sports | 1 | 0.5 | 0.01 | | Elec | 2 | 0.3 | 0.1 |

Citation

@inproceedings{zhou2023bootstrap,
author = {Zhou, Xin and Zhou, Hongyu and Liu, Yong and Zeng, Zhiwei and Miao, Chunyan and Wang, Pengwei and You, Yuan and Jiang, Feijun},
title = {Bootstrap Latent Representations for Multi-Modal Recommendation},
booktitle = {Proceedings of the ACM Web Conference 2023},
pages = {845–854},
year = {2023}
}