SSLRec: A Self-Supervised Learning Framework for Recommendation (2308.05697v3)

Abstract: Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms designed to provide state-of-the-art performance in various recommendation scenarios (e.g., graph collaborative filtering, sequential recommendation, social recommendation, KG-enhanced recommendation), there is still a lack of unified frameworks that integrate recommendation algorithms across different domains. Such a framework could serve as the cornerstone for self-supervised recommendation algorithms, unifying the validation of existing methods and driving the design of new ones. To address this gap, we introduce SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders. The SSLRec framework features a modular architecture that allows users to easily evaluate state-of-the-art models and a complete set of data augmentation and self-supervised toolkits to help create SSL recommendation models with specific needs. Furthermore, SSLRec simplifies the process of training and evaluating different recommendation models with consistent and fair settings. Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field. Our implemented SSLRec framework is available at the source code repository https://github.com/HKUDS/SSLRec.

• The paper introduces SSLRec, a unified self-supervised framework that benchmarks diverse recommendation algorithms.
• It employs modular augmentation modules and standardized interfaces to streamline training and evaluation while addressing data sparsity.
• Empirical results demonstrate state-of-the-art performance improvements in collaborative filtering, sequential, social, and KG-enhanced recommendations.

Essay on "SSLRec: A Self-Supervised Learning Framework for Recommendation"

The paper "SSLRec: A Self-Supervised Learning Framework for Recommendation" presents a structured and versatile framework designed to enhance the development and evaluation of self-supervised learning (SSL) approaches in recommendation systems. This work notably addresses the challenge of sparse and noisy data, which frequently limits the efficacy of traditional recommendation algorithms.

Core Contributions

The research introduces SSLRec, a framework serving as a unified benchmark platform that seamlessly incorporates multiple SSL algorithms across diverse recommendation scenarios such as graph collaborative filtering, sequential recommendation, social recommendation, and knowledge graph-enhanced recommendation. The core components of SSLRec are designed to facilitate the creation, training, and evaluation of state-of-the-art SSL-enhanced recommenders.

Framework Architecture

The framework is characterized by a highly modular architecture:

• Augmentation Modules: SSLRec distills commonly used augmentation techniques into reusable components, supporting both data-based and feature-based augmentation methods. This modular approach allows for rapid reproduction of existing SSL algorithms and the innovation of new augmentation techniques.
• Self-Supervised Objectives: SSLRec incorporates multiple SSL paradigms, including contrastive, generative, and predictive self-supervision, providing versatile self-supervised objectives through standardized loss functions.
• Standardized Interfaces: The framework adheres to a standardized interface for algorithm implementation, making integration with other modules efficient and uniform.

Unified Execution Process

SSLRec ensures a comprehensive and standardized algorithm execution process involving:

• Data Flow: It establishes a consistent data flow for processing varied data types across different scenarios, allowing for streamlined data handling and seamless integration with pre-existing modules.
• Training and Evaluation: The framework includes a robust trainer module that manages the entire training process while ensuring fair comparison conditions across various SSL algorithms.
• Hyperparameter Tuning: Automated hyperparameter tuning is facilitated through unified configurations, enhancing reproducibility and the efficiency of experiments.

Empirical Evaluation

Experiments conducted across multiple datasets in different recommendation scenarios demonstrate the framework's capability to reproduce state-of-the-art results. Notably, SSLRec provides benchmark results for several recommendation scenarios:

1. General Collaborative Filtering: SSLRec highlights the improved performance of SSL approaches using graph neural networks and emerging algorithms such as AutoCF that focus on adaptive supervision signals.
2. Sequential Recommendation: The framework reveals the superiority of methods like DCRec, which leverage collaborative signals alongside sequence data, demonstrating the benefit of integrated SSL methodologies.
3. Social Recommendation: Experiments confirm the importance of minimizing noise in social data while incorporating global and local information, as seen in methods like DSL.
4. KG-Enhanced Recommendation: The integration of knowledge graph features through SSL approaches such as KGCL demonstrates enhanced performance by effectively addressing data sparsity.
5. Multi-Behavior Recommendation: Results show improved predictions of target behaviors by leveraging auxiliary user actions, with methods like KMCLR effectively mitigating data sparsity issues.

Implications and Future Directions

The development of SSLRec is a significant contribution to the field, providing a critical toolset for researchers focused on enhancing recommendation systems through self-supervised learning. The modular nature and comprehensive coverage offer a platform for future innovations in SSL methods.

Future research could expand the framework's capabilities by integrating real-time data processing and exploring further optimization techniques. Additionally, the application of SSLRec in emerging domains such as cross-domain recommendations or privacy-preserving recommendations could open new avenues for exploration.

In summary, SSLRec represents a significant advancement in the standardization and application of self-supervised learning in recommendation systems, promoting greater efficiency and facilitating continued innovation.