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MMRec

$\text{MMRec}$: A modern MultiModal Recommendation toolbox that simplifies your research arXiv.
👉 Check our comprehensive survey on MMRec, arXiv.
👉 Check the awesome multimodal recommendation resources.

Toolbox

Supported Models

source code at: src\models

Model Paper Conference/Journal Code
General models
SelfCF SelfCF: A Simple Framework for Self-supervised Collaborative Filtering ACM TORS'23 selfcfed_lgn.py
LayerGCN Layer-refined Graph Convolutional Networks for Recommendation ICDE'23 layergcn.py
Multimodal models
VBPR VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback AAAI'16 vbpr.py
MMGCN MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video MM'19 mmgcn.py
ItemKNNCBF Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches RecSys'19 itemknncbf.py
GRCN Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback MM'20 grcn.py
MVGAE Multi-Modal Variational Graph Auto-Encoder for Recommendation Systems TMM'21 mvgae.py
DualGNN DualGNN: Dual Graph Neural Network for Multimedia Recommendation TMM'21 dualgnn.py
LATTICE Mining Latent Structures for Multimedia Recommendation MM'21 lattice.py
SLMRec Self-supervised Learning for Multimedia Recommendation TMM'22 slmrec.py
Newly added
BM3 Bootstrap Latent Representations for Multi-modal Recommendation WWW'23 bm3.py
FREEDOM A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation MM'23 freedom.py
MGCN Multi-View Graph Convolutional Network for Multimedia Recommendation MM'23 mgcn.py
DRAGON Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation ECAI'23 dragon.py
MG Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima WWW'24 trainer.py

Please consider to cite our paper if this framework helps you, thanks:

@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}
}

@article{zhou2023comprehensive,
      title={A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions}, 
      author={Hongyu Zhou and Xin Zhou and Zhiwei Zeng and Lingzi Zhang and Zhiqi Shen},
      year={2023},
      journal={arXiv preprint arXiv:2302.04473},
}

@article{zhou2023mmrecsm,
  author = {Zhou, Xin},
  title = {MMRec: Simplifying Multimodal Recommendation},
  year = {2023},
  journal={arXiv preprint arXiv:2302.03497},
}