MMRec

<div align="center"> <a href="https://github.com/enoche/MultimodalRecSys"><img width="300px" height="auto" src="https://github.com/enoche/MMRec/blob/master/images/logo.png"></a> </div>

$\text{MMRec}$: A modern <ins>M</ins>ulti<ins>M</ins>odal <ins>Rec</ins>ommendation toolbox that simplifies your research arXiv.
:point_right: Check our comprehensive survey on MMRec, arXiv.
:point_right: Check the awesome multimodal recommendation resources.

Toolbox

<p> <img src="./images/MMRec.png" width="500"> </p>

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 | common/trainer.py | | LGMRec | LGMRec: Local and Global Graph Learning for Multimodal Recommendation | AAAI'24 | lgmrec.py | | DA-MRS | Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback | KDD'24 | damrs.py | | SMORE | Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation | WSDM'25 | smore.py | | PGL | Mind Individual Information! Principal Graph Learning for Multimedia Recommendation | AAAI'25 | pgl.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}
}