SELFRec is a Python framework for self-supervised recommendation (SSR) which integrates commonly used datasets and metrics, and implements many state-of-the-art SSR models. SELFRec has a lightweight architecture and provides user-friendly interfaces. It can facilitate model implementation and evaluation.
Founder and principal contributor: @Coder-Yu @xiaxin1998

This repo is released with our survey paper on self-supervised learning for recommender systems. We organized a tutorial on self-supervised recommendation at WWW'22. Visit the tutorial page for more information.

Supported by:
Prof. Hongzhi Yin, The University of Queensland, Australia, h.yin1@uq.edu.au
Prof. Shazia Sadiq, ARC Training Centre for Information Resilience (CIRES), University of Queensland, Australia

Architecture

Features

• Fast execution: SELFRec is compatible with Python 3.8+, Tensorflow 1.14+, and PyTorch 1.8+ and powered by GPUs. We also optimize the time-consuming item ranking procedure, drastically reducing ranking time to seconds.
• Easy configuration: SELFRec provides simple and high-level interfaces, making it easy to add new SSR models in a plug-and-play fashion.
• Highly Modularized: SELFRec is divided into multiple discrete and independent modules. This design decouples model design from other procedures, allowing users to focus on the logic of their method and streamlining development.
• SSR-Specific: SELFRec is designed specifically for SSR. It provides specific modules and interfaces for rapid development of data augmentation and self-supervised tasks.

Requirements

numba==0.53.1
numpy==1.20.3
scipy==1.6.2
tensorflow==1.14.0
torch>=1.7.0

Usage

1. Configure the xx.conf file in the directory named conf. (xx is the name of the model you want to run)
2. Run main.py and choose the model you want to run.

Implemented Models

Model	Paper	Type	Code
SASRec	Kang et al. Self-Attentive Sequential Recommendation, ICDM'18.	Sequential	PyTorch
CL4SRec	Xie et al. Contrastive Learning for Sequential Recommendation, ICDE'22.	Sequential	PyTorch
BERT4Rec	Sun et al. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer, CIKM'19.	Sequential	PyTorch

Model	Paper	Type	Code
XSimGCL	Yu et al. XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation, TKDE'23.	Graph + CL	PyTorch
SimGCL	Yu et al. Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation, SIGIR'22.	Graph + CL	PyTorch
DirectAU	Wang et al. Towards Representation Alignment and Uniformity in Collaborative Filtering, KDD'22.	Graph	PyTorch
NCL	Lin et al. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning, WWW'22.	Graph + CL	PyTorch
MixGCF	Huang et al. MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems, KDD'21.	Graph + DA	PyTorch
MHCN	Yu et al. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation, WWW'21.	Graph + CL	TensorFlow
SGL	Wu et al. Self-supervised Graph Learning for Recommendation, SIGIR'21.	Graph + CL	TensorFlow & Torch
SEPT	Yu et al. Socially-Aware Self-supervised Tri-Training for Recommendation, KDD'21.	Graph + CL	TensorFlow
BUIR	Lee et al. Bootstrapping User and Item Representations for One-Class Collaborative Filtering, SIGIR'21.	Graph + DA	PyTorch
SSL4Rec	Yao et al. Self-supervised Learning for Large-scale Item Recommendations, CIKM'21.	Graph + CL	PyTorch
SelfCF	Zhou et al. SelfCF: A Simple Framework for Self-supervised Collaborative Filtering, arXiv'21.	Graph + DA	PyTorch
LightGCN	He et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, SIGIR'20.	Graph	PyTorch
MF	Yehuda et al. Matrix Factorization Techniques for Recommender Systems, IEEE Computer'09.	Graph	PyTorch

* CL is short for contrastive learning (including data augmentation); DA is short for data augmentation only

Leaderboard

The results are obtained on the dataset of Yelp2018. We performed grid search for the best hyperparameters.
General hyperparameter settings are: batch_size: 2048, emb_size: 64, learning rate: 0.001, L2 reg: 0.0001.

Model	Recall@20	NDCG@20	Hyperparameter settings
MF	0.0543	0.0445
LightGCN	0.0639	0.0525	layer=3
NCL	0.0670	0.0562	layer=3, ssl_reg=1e-6, proto_reg=1e-7, tau=0.05, hyper_layers=1, alpha=1.5, num_clusters=2000
SGL	0.0675	0.0555	λ=0.1, ρ=0.1, tau=0.2 layer=3
MixGCF	0.0691	0.0577	layer=3, n_nes=64, layer=3
DirectAU	0.0695	0.0583	𝛾=2, layer=3
SimGCL	0.0721	0.0601	λ=0.5, eps=0.1, tau=0.2, layer=3
XSimGCL	0.0723	0.0604	λ=0.2, eps=0.2, l∗=1 tau=0.15 layer=3

Implement Your Model

1. Create a .conf file for your model in the directory named conf.
2. Make your model inherit the proper base class.
3. Reimplement the following functions.
   • build(), train(), save(), predict()
4. Register your model in main.py.

Related Datasets

   

Data Set	Basic Meta					User Context
	Users	Items	Ratings (Scale)		Density	Users	Links (Type)
Douban	2,848	39,586	894,887	[1, 5]	0.794%	2,848	35,770	Trust
LastFM	1,892	17,632	92,834	implicit	0.27%	1,892	25,434	Trust
Yelp	19,539	21,266	450,884	implicit	0.11%	19,539	864,157	Trust
Amazon-Book	52,463	91,599	2,984,108	implicit	0.11%	-	-	-

Reference

If you find this repo helpful to your research, please cite our paper.

@article{yu2023self,
  title={Self-supervised learning for recommender systems: A survey},
  author={Yu, Junliang and Yin, Hongzhi and Xia, Xin and Chen, Tong and Li, Jundong and Huang, Zi},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2023},
  publisher={IEEE}
}