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.

翻译：自监督学习作为解决推荐系统中数据稀疏和噪声问题的手段，近年来受到广泛关注。尽管针对不同推荐场景（如图协同过滤、序列推荐、社交推荐、知识图谱增强推荐）已涌现出大量能实现最先进性能的自监督学习算法，但当前仍缺乏能够整合跨领域推荐算法的统一框架。此类框架可成为自监督推荐算法的基石，统一现有方法的验证体系，并推动新算法的设计。为此，我们提出SSLRec这一新型基准平台，其提供标准化、灵活且全面的评估框架，用于评测各类自监督增强推荐器。SSLRec框架采用模块化架构，支持用户便捷评估最先进模型，并配备完整的数据增强与自监督工具包，可针对特定需求构建自监督推荐模型。此外，SSLRec通过一致性公平设置，简化了不同推荐模型的训练与评估流程。该平台覆盖跨场景的全套最新自监督增强推荐模型，助力研究人员评估前沿成果并推动领域创新。SSLRec框架已开源至代码仓库 https://github.com/HKUDS/SSLRec。