ImRec:Implicit feedback based Recommendation framework

We opensource the framework and hope it could benefit the community. Please open an issue if you have difficulty in reproducing the results.

• :twisted_rightwards_arrows: All my models (LayerGCN/SelfCF) are further integrated into the unified MMRec framework.

Features

• Aligning recommendation to Industry

  • Global time splitting with timestamp (Same raw source data, same results! Previous random splittings suffer from data leakage and do not hold this property.)
  • Strictly predict future interactions.

• Reproducibility Ensured: Same seed, same server(GPU), same performance.

• Supporting various supervised tasks

  • Supervised with sampled negatives by a sampling strategy
  • Supervised with all positives and negatives (All unobserved are negatives)
  • Self-supervised with observed interactions only

• Unified and order-invariant grid search (GS) entry

  • One entry for grid search and per-run of model
  • Reproduce same results no matter what order of hyper-parameters in GS
  • Results are summarized to ease your manual comparison after GS

• Personal maintenance, towards bug-free

  • Framework consistency is ensured

Supported models (More to expect):

- All source codes are under models dir
- You may post an issue for the model you expected an integration, I'll handle it if the source code is available.

Model	Paper (PDF)	Conference/Journal	Code
BPR	BPR: Bayesian Personalized Ranking from Implicit Feedback	UAI'09	bpr.py
MultiVAE	Variational Autoencoders for Collaborative Filtering	WWW'18	multivae.py
NGCF	Neural Graph Collaborative Filtering	SIGIR'19	ngcf.py
EHCF	Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation	AAAI'20	ehcf.py
LR-GCCF	Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach	AAAI'20	lrgccf.py
LightGCN	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	SIGIR'20	lightgcn.py
BUIR	Bootstrapping User and Item Representations for One-Class Collaborative Filtering	SIGIR'21	buir*.py
UltraGCN	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	CIKM'21	ultragcn.py
IMP-GCN	Interest-aware Message-Passing GCN for Recommendation	WWW'21	imp_gcn.py
Newly added
:fire: LayerGCN	Layer-refined Graph Convolutional Networks for Recommendation	ICDE'23	layergcn.py
SelfCF(ed/he/ep)	SelfCF: A Simple Framework for Self-supervised Collaborative Filtering	ACM TORS'23	selfcf*.py

LayerGCN: Layer-refined Graph Convolutional Networks for Recommendation

Data

Download from Google Drive: Amazon-Vedio-Games/Food etc.

How to run

python main.py -m LayerGCN -d food

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 LayerGCN to reproduce the results in Table II of our paper as:

Datasets	dropout	reg_weight
MOOC	0.1	1e-03
Games	0.2	1e-03
Food	0.1	1e-02
Yelp	0.2	1e-03

Citing if this repo. helps:

@inproceedings{zhou2023layer,
  title={Layer-refined graph convolutional networks for recommendation},
  author={Zhou, Xin and Lin, Donghui and Liu, Yong and Miao, Chunyan},
  booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)},
  pages={1247--1259},
  year={2023},
  organization={IEEE}
}


@article{zhou2023selfcf,
  author = {Zhou, Xin and Sun, Aixin and Liu, Yong and Zhang, Jie and Miao, Chunyan},
  title = {SelfCF: A Simple Framework for Self-Supervised Collaborative Filtering},
  year = {2023},
  journal = {ACM Trans. Recomm. Syst.},
  publisher = {Association for Computing Machinery},
}

• This repo. simplifies Recbole to align with industry settings.