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
LGMRec	LGMRec: Local and Global Graph Learning for Multimodal Recommendation	AAAI'24	lgmrec.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},
}

@inproceedings{zhou2023mmrec,
  title={Mmrec: Simplifying multimodal recommendation},
  author={Zhou, Xin},
  booktitle={Proceedings of the 5th ACM International Conference on Multimedia in Asia Workshops},
  pages={1--2},
  year={2023}
}