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

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},
}