User Guide • Models • Datasets
SSLRec is a PyTorch-based open-source deep learning framework for recommender systems enhanced by self-supervised learning techniques. It's user-friendly and contains commonly-used datasets, code scripts for data processing, training, testing, evaluation, and state-of-the-art research models. SSLRec offers a vast array of utility functions and an easy-to-use interface that simplifies the development and evaluation of recommendation models.
Our library includes various self-supervised learning recommendation algorithms, covering five major categories:
- General Collaborative Filtering
- Sequential Recommendation
- Multi-Behavior Recommendation
- Social Recommendation
- Knowledge Graph-enhanced Recommendation
Our framework offers a unified training, validation, and testing process for each category, along with a standardized data preprocessing method using publicly available datasets. This makes it easy to reproduce various models and enables fair comparisons between different methods.
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🧩Flexible Modular Architecture. The SSLRec library features a modular architecture that allows for effortless customization and combination of modules. This enables users to create personalized recommendation models that fit their specific needs and requirements.
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🌟Diverse Recommendation Scenarios. The SSLRec library is a versatile tool for researchers and practitioners who are interested in building effective recommendation models across diverse recommender system research lines.
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💡Comprehensive State-of-the-Art Models. Our SSLRec framework offers a wide range of SSL-enhanced recommendation models for various scenarios. Researchers can evaluate these models using advanced techniques and use them as a foundation for driving innovation in the field of recommender systems.
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📊Unified Data Feeder and Standard Evaluation Protocols. The SSLRec framework features a unified data feeder and standard evaluation protocols that enable easy loading and preprocessing of data from various sources and formats, while ensuring objective and fair evaluation of recommendation models.
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🛠️Rich Utility Functions. The SSLRec library provides a vast array of utility functions that simplify the development and evaluation of recommendation models. These functions incorporate common functionalities of recommender systems and self-supervised learning for graph operations, network architectures, and loss functions.
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🤖Easy-to-Use Interface. We offer a user-friendly interface that streamlines the training and evaluation of recommendation models. This allows researchers and practitioners to experiment with various models and configurations with ease and efficiency.
🔥 2023-06-28: Early stop is now feasible in the training strategy. (Check Here for more details)
We are committed to continuously adding new self-supervised models to the SSLRec framework to keep up with the latest developments in the field of recommender systems. Stay tuned for updates! 🔍
Here, we list the implemented models in abbrevation. For more detailed information, please refer to Models.
General Collaborative Filtering
- LightGCN (SIGIR'20), SGL (SIGIR'21), HCCF (SIGIR'22), SimGCL (SIGIR'22), NCL (WWW'22), DirectAU (KDD'22), LightGCL (ICLR'23), AutoCF (WWW'23), DCCF (SIGIR'23), GFormer (SIGIR'23)
Sequential Recommendation
Social Recommendation
Knowledge Graph-enhanced Recommendation
Multi-behavior Recommendation
SSLRec is implemented under the following development environment:
- python==3.10.4
- numpy==1.22.3
- torch==1.11.0
- scipy==1.7.3
You can easily train LightGCN using our framework by running the following script:
python main.py --model LightGCN
This script will run the LightGCN model on the yelp datasets.
The training configuration for LightGCN is saved in lightgcn.yml. You can modify the values in this file to achieve different training effects. Furthermore, if you're interested in trying out other implemented models, you can find a list of them under Models, and easily replace LightGCN with your model of choice.
For users who wish to gain a deeper understanding, we recommend reading our User Guide. This guide provides comprehensive explanations of SSLRec's concepts and usage, including:
- SSLRec framework architecture design
- Implementing your own model in SSLRec
- Deploying your own datasets in SSLRec
- Implementing your own training process in SSLRec
- Automatic hyperparameter tuning in SSLRec
and so on.
If you come across any bugs or have suggestions for improvement, please feel free to let us know by filing an issue.
We warmly welcome contributions of all kinds, from bug fixes to new features and extensions. 🙌