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https://github.com/coder-yu/selfrec
An open-source framework for self-supervised recommender systems.
https://github.com/coder-yu/selfrec
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An open-source framework for self-supervised recommender systems.
- Host: GitHub
- URL: https://github.com/coder-yu/selfrec
- Owner: Coder-Yu
- Created: 2021-09-14T14:15:59.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-08T10:31:48.000Z (4 months ago)
- Last Synced: 2025-01-04T18:04:22.059Z (8 days ago)
- Language: Python
- Homepage:
- Size: 32.1 MB
- Stars: 533
- Watchers: 7
- Forks: 79
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
**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 ](https://github.com/Coder-Yu) [@xiaxin1998](https://github.com/xiaxin1998)
This repo is released with our [survey paper](https://arxiv.org/abs/2203.15876) on self-supervised learning for recommender systems. We organized a tutorial on self-supervised recommendation at WWW'22. Visit the [tutorial page](https://ssl-recsys.github.io/) for more information.
**Supported by**:
Prof. Hongzhi Yin, The University of Queensland, Australia, [email protected]
Prof. Shazia Sadiq, ARC Training Centre for Information Resilience (CIRES), University of Queensland, Australia
Architecture
Features
-
Fast execution: SELFRec is compatible with Python 3.9+, Tensorflow 1.14+ (optional), 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.
How to Use
- Execute pip install -r requirements.txt under the SELFRec directory
- Configure the xx.yaml file in ./conf . (xx is the name of the model you want to run)
- 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 **.yaml** 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%
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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}
}
```